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pdf2dataset

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7758f6665e6f1e254a1c5ec32c77b1c5

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PDF2Dataset 10

(norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_atom_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_bond_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (act_func_node): PRe LU(num_parameters=1) (act_func_edge): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) ) ) (readout): Readout() (mol_atom_from_atom_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (mol_atom_from_bond_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (sigmoid): Sigmoid() ) Number of parameters = 889,418 Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0000 loss_train: 1. 027524 loss_val: 0. 494863 auc_val: 0. 8744 cur_lr: 0. 00059 t_time: 5. 5550s v_time: 0. 73 79s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0001 loss_train: 0. 855072 loss_val: 0. 488093 auc_val: 0. 8805 cur_lr: 0. 00098 t_time: 5. 4703s v_time: 0. 74 35s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0002 loss_train: 0. 802001 loss_val: 0. 488020 auc_val: 0. 8953 cur_lr: 0. 00073 t_time: 5. 5585s v_time: 0. 73 17s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0003 loss_train: 0. 743282 loss_val: 0. 483438 auc_val: 0. 8804 cur_lr: 0. 00055 t_time: 5. 5933s v_time: 0. 73 05s

deepchem.pdf

/usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0004 loss_train: 0. 705130 loss_val: 0. 473394 auc_val: 0. 9043 cur_lr: 0. 00041 t_time: 5. 5970s v_time: 0. 75 58s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0005 loss_train: 0. 682583 loss_val: 0. 473367 auc_val: 0. 8962 cur_lr: 0. 00030 t_time: 5. 5740s v_time: 0. 72 44s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0006 loss_train: 0. 659755 loss_val: 0. 477886 auc_val: 0. 8939 cur_lr: 0. 00023 t_time: 5. 5852s v_time: 0. 72 88s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0007 loss_train: 0. 658016 loss_val: 0. 476979 auc_val: 0. 8923 cur_lr: 0. 00017 t_time: 5. 5050s v_time: 0. 72 80s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0008 loss_train: 0. 647427 loss_val: 0. 470443 auc_val: 0. 9020 cur_lr: 0. 00013 t_time: 5. 5287s v_time: 0. 72 95s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0009 loss_train: 0. 646125 loss_val: 0. 474078 auc_val: 0. 8938 cur_lr: 0. 00010 t_time: 5. 7616s v_time: 0. 72 85s Model 0 best validation auc = 0. 904320 on epoch 4 Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight".

deepchem.pdf

Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Loading pretrained parameter "readout. cached_zero_vector". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_atom_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. bias". Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Model 0 test auc = 0. 921247 Ensemble test auc = 0. 921247 Fold 1 Loading data Number of tasks = 1 Splitting data with seed 1

deepchem.pdf

100% 2039/2039 [00:00<00:00, 3551. 50it/s] Total scaffolds = 1,025 | train scaffolds = 768 | val scaffolds = 132 | test scaffolds = 125 Label averages per scaffold, in decreasing order of scaffold frequency,capped at 10 scaffolds and 20 labels: [(a rray([0. 72992701]), array([137])), (array([1. ]), array([2])), (array([1. ]), array([3])), (array([0. 8]), array([5 ])), (array([1. ]), array([9])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array([1. ]), array([1]))] Class sizes p_np 0: 23. 49%, 1: 76. 51% Total size = 2,039 | train size = 1,631 | val size = 203 | test size = 205 Loading model 0 from model/tryout/model. ep3 Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Pretrained parameter "av_task_atom. linear. weight" cannot be found in model parameters. Pretrained parameter "av_task_atom. linear. bias" cannot be found in model parameters. Pretrained parameter "av_task_bond. linear. weight" cannot be found in model parameters.

deepchem.pdf

Pretrained parameter "av_task_bond. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear. weight" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear_rev. weight" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear_rev. bias" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear. weight" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear_rev. weight" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear_rev. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. readout. cached_zero_vector" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_atom. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_atom. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_bond. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_bond. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_atom. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_atom. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_bond. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_bond. bias" cannot be found in model parameters. Grover Finetune Task( (grover): GROVEREmbedding( (encoders): GTrans Encoder( (edge_blocks): Module List( (0): MTBlock( (heads): Module List( (0): Head( (mpn_q): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_k): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_v): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) ) ) (act_func): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) (layernorm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (W_i): Linear(in_features=165, out_features=100, bias=False) (attn): Multi Headed Attention( (linear_layers): Module List( (0): Linear(in_features=100, out_features=100, bias=True) (1): Linear(in_features=100, out_features=100, bias=True) (2): Linear(in_features=100, out_features=100, bias=True) ) (output_linear): Linear(in_features=100, out_features=100, bias=False) (attention): Attention() (dropout): Dropout(p=0. 1, inplace=False) ) (W_o): Linear(in_features=100, out_features=100, bias=False) (sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) ) (node_blocks): Module List( (0): MTBlock( (heads): Module List( (0): Head( (mpn_q): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_k): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_v): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) )

deepchem.pdf

) (act_func): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) (layernorm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (W_i): Linear(in_features=151, out_features=100, bias=False) (attn): Multi Headed Attention( (linear_layers): Module List( (0): Linear(in_features=100, out_features=100, bias=True) (1): Linear(in_features=100, out_features=100, bias=True) (2): Linear(in_features=100, out_features=100, bias=True) ) (output_linear): Linear(in_features=100, out_features=100, bias=False) (attention): Attention() (dropout): Dropout(p=0. 1, inplace=False) ) (W_o): Linear(in_features=100, out_features=100, bias=False) (sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) ) (ffn_atom_from_atom): Positionwise Feed Forward( (W_1): Linear(in_features=251, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (ffn_atom_from_bond): Positionwise Feed Forward( (W_1): Linear(in_features=251, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (ffn_bond_from_atom): Positionwise Feed Forward( (W_1): Linear(in_features=265, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (ffn_bond_from_bond): Positionwise Feed Forward( (W_1): Linear(in_features=265, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (atom_from_atom_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (atom_from_bond_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_atom_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_bond_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (act_func_node): PRe LU(num_parameters=1) (act_func_edge): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) ) ) (readout): Readout() (mol_atom_from_atom_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (mol_atom_from_bond_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (sigmoid): Sigmoid()

deepchem.pdf

) Number of parameters = 889,418 Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0000 loss_train: 1. 016377 loss_val: 0. 492704 auc_val: 0. 8791 cur_lr: 0. 00059 t_time: 6. 3182s v_time: 0. 77 94s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0001 loss_train: 0. 822924 loss_val: 0. 487600 auc_val: 0. 8680 cur_lr: 0. 00098 t_time: 5. 5121s v_time: 0. 79 89s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0002 loss_train: 0. 752341 loss_val: 0. 470391 auc_val: 0. 8893 cur_lr: 0. 00073 t_time: 5. 5443s v_time: 0. 76 47s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0003 loss_train: 0. 709847 loss_val: 0. 468552 auc_val: 0. 8863 cur_lr: 0. 00055 t_time: 5. 6165s v_time: 0. 81 04s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0004 loss_train: 0. 682037 loss_val: 0. 463301 auc_val: 0. 8895 cur_lr: 0. 00041 t_time: 5. 5689s v_time: 0. 77 95s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0005 loss_train: 0. 659133 loss_val: 0. 464382 auc_val: 0. 8914 cur_lr: 0. 00030 t_time: 5. 5949s v_time: 0. 80 20s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r

deepchem.pdf

unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0006 loss_train: 0. 630823 loss_val: 0. 463676 auc_val: 0. 8871 cur_lr: 0. 00023 t_time: 5. 5311s v_time: 0. 75 48s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0007 loss_train: 0. 613836 loss_val: 0. 460376 auc_val: 0. 8912 cur_lr: 0. 00017 t_time: 5. 5768s v_time: 0. 75 11s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0008 loss_train: 0. 604636 loss_val: 0. 464385 auc_val: 0. 8900 cur_lr: 0. 00013 t_time: 5. 5764s v_time: 0. 78 48s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0009 loss_train: 0. 600993 loss_val: 0. 461464 auc_val: 0. 8902 cur_lr: 0. 00010 t_time: 5. 6025s v_time: 0. 77 36s Model 0 best validation auc = 0. 891352 on epoch 5 Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias".

deepchem.pdf

Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Loading pretrained parameter "readout. cached_zero_vector". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_atom_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. bias". Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Model 0 test auc = 0. 920000 Ensemble test auc = 0. 920000 Fold 2 Loading data Number of tasks = 1 Splitting data with seed 2 100% 2039/2039 [00:00<00:00, 3569. 05it/s] Total scaffolds = 1,025 | train scaffolds = 766 | val scaffolds = 125 | test scaffolds = 134 Label averages per scaffold, in decreasing order of scaffold frequency,capped at 10 scaffolds and 20 labels: [(a rray([0. 72992701]), array([137])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array( [1. ]), array([1] )), (array([0. ]), array([1])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array([1. ]), array([1])), (array([0. ]), array([5])), (array([1. ]), array([1]))] Class sizes p_np 0: 23. 49%, 1: 76. 51% Total size = 2,039 | train size = 1,631 | val size = 203 | test size = 205 Loading model 0 from model/tryout/model. ep3 Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight".

deepchem.pdf

Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Pretrained parameter "av_task_atom. linear. weight" cannot be found in model parameters. Pretrained parameter "av_task_atom. linear. bias" cannot be found in model parameters. Pretrained parameter "av_task_bond. linear. weight" cannot be found in model parameters. Pretrained parameter "av_task_bond. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear. weight" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear_rev. weight" cannot be found in model parameters. Pretrained parameter "bv_task_atom. linear_rev. bias" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear. weight" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear. bias" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear_rev. weight" cannot be found in model parameters. Pretrained parameter "bv_task_bond. linear_rev. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. readout. cached_zero_vector" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_atom. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_atom. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_bond. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_atom_from_bond. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_atom. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_atom. bias" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_bond. weight" cannot be found in model parameters. Pretrained parameter "fg_task_all. linear_bond_from_bond. bias" cannot be found in model parameters. Grover Finetune Task( (grover): GROVEREmbedding( (encoders): GTrans Encoder( (edge_blocks): Module List( (0): MTBlock( (heads): Module List( (0): Head( (mpn_q): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_k): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False)

deepchem.pdf

(act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_v): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) ) ) (act_func): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) (layernorm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (W_i): Linear(in_features=165, out_features=100, bias=False) (attn): Multi Headed Attention( (linear_layers): Module List( (0): Linear(in_features=100, out_features=100, bias=True) (1): Linear(in_features=100, out_features=100, bias=True) (2): Linear(in_features=100, out_features=100, bias=True) ) (output_linear): Linear(in_features=100, out_features=100, bias=False) (attention): Attention() (dropout): Dropout(p=0. 1, inplace=False) ) (W_o): Linear(in_features=100, out_features=100, bias=False) (sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) ) (node_blocks): Module List( (0): MTBlock( (heads): Module List( (0): Head( (mpn_q): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_k): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) (mpn_v): MPNEncoder( (dropout_layer): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) (W_h): Linear(in_features=100, out_features=100, bias=False) ) ) ) (act_func): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) (layernorm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (W_i): Linear(in_features=151, out_features=100, bias=False) (attn): Multi Headed Attention( (linear_layers): Module List( (0): Linear(in_features=100, out_features=100, bias=True) (1): Linear(in_features=100, out_features=100, bias=True) (2): Linear(in_features=100, out_features=100, bias=True) ) (output_linear): Linear(in_features=100, out_features=100, bias=False) (attention): Attention() (dropout): Dropout(p=0. 1, inplace=False) ) (W_o): Linear(in_features=100, out_features=100, bias=False) (sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) ) (ffn_atom_from_atom): Positionwise Feed Forward( (W_1): Linear(in_features=251, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (ffn_atom_from_bond): Positionwise Feed Forward( (W_1): Linear(in_features=251, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False)

deepchem.pdf

(act_func): PRe LU(num_parameters=1) ) (ffn_bond_from_atom): Positionwise Feed Forward( (W_1): Linear(in_features=265, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (ffn_bond_from_bond): Positionwise Feed Forward( (W_1): Linear(in_features=265, out_features=400, bias=True) (W_2): Linear(in_features=400, out_features=100, bias=True) (dropout): Dropout(p=0. 1, inplace=False) (act_func): PRe LU(num_parameters=1) ) (atom_from_atom_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (atom_from_bond_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_atom_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (bond_from_bond_sublayer): Sublayer Connection( (norm): Layer Norm((100,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (act_func_node): PRe LU(num_parameters=1) (act_func_edge): PRe LU(num_parameters=1) (dropout_layer): Dropout(p=0. 1, inplace=False) ) ) (readout): Readout() (mol_atom_from_atom_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (mol_atom_from_bond_ffn): Sequential( (0): Dropout(p=0. 1, inplace=False) (1): Linear(in_features=300, out_features=200, bias=True) (2): PRe LU(num_parameters=1) (3): Dropout(p=0. 1, inplace=False) (4): Linear(in_features=200, out_features=1, bias=True) ) (sigmoid): Sigmoid() ) Number of parameters = 889,418 Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0000 loss_train: 1. 000364 loss_val: 0. 507716 auc_val: 0. 8434 cur_lr: 0. 00059 t_time: 5. 7976s v_time: 0. 78 02s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0001 loss_train: 0. 824395 loss_val: 0. 504539 auc_val: 0. 8560 cur_lr: 0. 00098 t_time: 5. 5894s v_time: 0. 77 79s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked))

deepchem.pdf

/usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0002 loss_train: 0. 735137 loss_val: 0. 493423 auc_val: 0. 8539 cur_lr: 0. 00073 t_time: 5. 5191s v_time: 0. 76 10s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0003 loss_train: 0. 687535 loss_val: 0. 487282 auc_val: 0. 8597 cur_lr: 0. 00055 t_time: 5. 5613s v_time: 0. 75 95s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0004 loss_train: 0. 681197 loss_val: 0. 489330 auc_val: 0. 8702 cur_lr: 0. 00041 t_time: 5. 5513s v_time: 0. 75 01s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0005 loss_train: 0. 647608 loss_val: 0. 488870 auc_val: 0. 8618 cur_lr: 0. 00030 t_time: 5. 6565s v_time: 0. 77 39s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0006 loss_train: 0. 638494 loss_val: 0. 488281 auc_val: 0. 8729 cur_lr: 0. 00023 t_time: 5. 5400s v_time: 0. 75 84s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0007 loss_train: 0. 626862 loss_val: 0. 490144 auc_val: 0. 8702 cur_lr: 0. 00017 t_time: 5. 6183s v_time: 0. 78 14s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0008 loss_train: 0. 619776 loss_val: 0. 484179 auc_val: 0. 8782 cur_lr: 0. 00013 t_time: 5. 9662s v_time: 0. 75 96s /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 10 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller th an what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.

deepchem.pdf

cpuset_checked)) /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Epoch: 0009 loss_train: 0. 613262 loss_val: 0. 486484 auc_val: 0. 8789 cur_lr: 0. 00010 t_time: 6. 3030s v_time: 0. 79 31s Model 0 best validation auc = 0. 878887 on epoch 9 Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Loading pretrained parameter "readout. cached_zero_vector". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_atom_ffn. 2. weight".

deepchem.pdf

Loading pretrained parameter "mol_atom_from_atom_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. bias". Moving model to cuda /usr/local/lib/python3. 7/dist-packages/torch/utils/data/dataloader. py:481: User Warning: This Data Loader will cre ate 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller tha n what this Data Loader is going to create. Please be aware that excessive worker creation might get Data Loader r unning slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary. cpuset_checked)) Model 0 test auc = 0. 888635 Ensemble test auc = 0. 888635 3-fold cross validation Seed 0 ==> test auc = 0. 921247 Seed 1 ==> test auc = 0. 920000 Seed 2 ==> test auc = 0. 888635 overall_scaffold_balanced_test_auc=0. 909961 std=0. 015088 Predicting output Extracting molecular features If the finetuned model uses the molecular feature as input, we need to generate the molecular feature for the target molecules as well. ! python scripts / save_features. py --data_path exampledata / finetune / bbbp. csv \ --save_path exampledata / finetune / bbbp. npz \ --features_generator rdkit_2d_normalized \ --restart WARNING:root:No normalization for BCUT2D_MWHI WARNING:root:No normalization for BCUT2D_MWLOW WARNING:root:No normalization for BCUT2D_CHGHI WARNING:root:No normalization for BCUT2D_CHGLO WARNING:root:No normalization for BCUT2D_LOGPHI WARNING:root:No normalization for BCUT2D_LOGPLOW WARNING:root:No normalization for BCUT2D_MRHI WARNING:root:No normalization for BCUT2D_MRLOW [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors

deepchem.pdf

[21:09:19] WARNING: not removing hydrogen atom without neighbors [21:09:19] WARNING: not removing hydrogen atom without neighbors 0% 6/2039 [00:01<06:32, 5. 18it/s][21:09:21] WARNING: not removing hydrogen atom without neighbors 2% 47/2039 [00:02<01:03, 31. 22it/s][21:09:22] WARNING: not removing hydrogen atom without neighbors 4% 91/2039 [00:04<01:14, 26. 15it/s][21:09:24] WARNING: not removing hydrogen atom without neighbors 6% 120/2039 [00:05<01:03, 30. 16it/s][21:09:25] WARNING: not removing hydrogen atom without neighbors 8% 161/2039 [00:07<01:14, 25. 26it/s][21:09:27] WARNING: not removing hydrogen atom without neighbors 8% 169/2039 [00:08<02:01, 15. 42it/s][21:09:28] WARNING: not removing hydrogen atom without neighbors [21:09:28] WARNING: not removing hydrogen atom without neighbors 10% 198/2039 [00:08<01:00, 30. 28it/s][21:09:29] WARNING: not removing hydrogen atom without neighbors 13% 265/2039 [00:10<00:55, 32. 07it/s][21:09:31] WARNING: not removing hydrogen atom without neighbors [21:09:31] WARNING: not removing hydrogen atom without neighbors [21:09:31] WARNING: not removing hydrogen atom without neighbors 15% 305/2039 [00:12<01:07, 25. 66it/s][21:09:32] WARNING: not removing hydrogen atom without neighbors [21:09:32] WARNING: not removing hydrogen atom without neighbors [21:09:32] WARNING: not removing hydrogen atom without neighbors 20% 417/2039 [00:16<00:46, 35. 03it/s][21:09:37] WARNING: not removing hydrogen atom without neighbors [21:09:37] WARNING: not removing hydrogen atom without neighbors [21:09:38] WARNING: not removing hydrogen atom without neighbors 23% 464/2039 [00:18<00:48, 32. 41it/s][21:09:39] WARNING: not removing hydrogen atom without neighbors 26% 529/2039 [00:21<00:52, 28. 80it/s][21:09:41] WARNING: not removing hydrogen atom without neighbors 27% 552/2039 [00:22<00:56, 26. 22it/s][21:09:42] WARNING: not removing hydrogen atom without neighbors 28% 570/2039 [00:22<00:58, 25. 26it/s][21:09:42] WARNING: not removing hydrogen atom without neighbors 29% 600/2039 [00:23<00:55, 25. 71it/s][21:09:43] WARNING: not removing hydrogen atom without neighbors 30% 619/2039 [00:24<00:55, 25. 71it/s][21:09:44] WARNING: not removing hydrogen atom without neighbors [21:09:44] WARNING: not removing hydrogen atom without neighbors 32% 645/2039 [00:25<00:49, 28. 14it/s][21:09:45] WARNING: not removing hydrogen atom without neighbors [21:09:45] WARNING: not removing hydrogen atom without neighbors 34% 687/2039 [00:27<00:57, 23. 64it/s][21:09:47] WARNING: not removing hydrogen atom without neighbors 35% 714/2039 [00:28<00:57, 23. 01it/s][21:09:48] WARNING: not removing hydrogen atom without neighbors 39% 798/2039 [00:31<00:58, 21. 09it/s][21:09:53] WARNING: not removing hydrogen atom without neighbors 40% 813/2039 [00:33<01:37, 12. 61it/s][21:09:54] WARNING: not removing hydrogen atom without neighbors 44% 897/2039 [00:36<00:49, 23. 10it/s][21:09:56] WARNING: not removing hydrogen atom without neighbors 44% 903/2039 [00:36<00:49, 22. 76it/s][21:09:57] WARNING: not removing hydrogen atom without neighbors 45% 919/2039 [00:37<00:44, 25. 44it/s][21:09:58] WARNING: not removing hydrogen atom without neighbors [21:09:58] [21:09:58] WARNING: not removing hydrogen atom without neighbors WARNING: not removing hydrogen atom without neighbors [21:09:59] WARNING: not removing hydrogen atom without neighbors 50% 1013/2039 [00:41<00:33, 30. 69it/s][21:10:01] WARNING: not removing hydrogen atom without neighbors 51% 1041/2039 [00:42<00:34, 29. 09it/s][21:10:02] WARNING: not removing hydrogen atom without neighbors 56% 1135/2039 [00:45<00:36, 24. 64it/s][21:10:05] WARNING: not removing hydrogen atom without neighbors 57% 1153/2039 [00:46<00:35, 24. 77it/s][21:10:06] WARNING: not removing hydrogen atom without neighbors 57% 1161/2039 [00:46<00:28, 30. 81it/s][21:10:07] WARNING: not removing hydrogen atom without neighbors 57% 1168/2039 [00:47<00:36, 23. 87it/s][21:10:07] WARNING: not removing hydrogen atom without neighbors 58% 1186/2039 [00:47<00:25, 33. 33it/s][21:10:07] WARNING: not removing hydrogen atom without neighbors 59% 1194/2039 [00:48<00:26, 32. 07it/s][21:10:08] WARNING: not removing hydrogen atom without neighbors 61% 1235/2039 [00:49<00:38, 21. 11it/s][21:10:10] WARNING: not removing hydrogen atom without neighbors 62% 1263/2039 [00:51<00:32, 24. 19it/s][21:10:11] WARNING: not removing hydrogen atom without neighbors [21:10:11] WARNING: not removing hydrogen atom without neighbors 62% 1268/2039 [00:51<00:32, 24. 06it/s][21:10:11] WARNING: not removing hydrogen atom without neighbors 63% 1292/2039 [00:52<00:28, 26. 37it/s][21:10:12] WARNING: not removing hydrogen atom without neighbors [21:10:12] WARNING: not removing hydrogen atom without neighbors 64% 1296/2039 [00:52<00:30, 24. 41it/s][21:10:12] WARNING: not removing hydrogen atom without neighbors 64% 1308/2039 [00:52<00:27, 26. 72it/s][21:10:12] WARNING: not removing hydrogen atom without neighbors 65% 1318/2039 [00:53<00:31, 22. 61it/s][21:10:13] WARNING: not removing hydrogen atom without neighbors 65% 1334/2039 [00:53<00:22, 31. 22it/s][21:10:13] WARNING: not removing hydrogen atom without neighbors 67% 1356/2039 [00:54<00:36, 18. 90it/s][21:10:14] WARNING: not removing hydrogen atom without neighbors 68% 1384/2039 [00:55<00:36, 17. 78it/s][21:10:15] WARNING: not removing hydrogen atom without neighbors 71% 1442/2039 [00:57<00:27, 22. 02it/s][21:10:18] WARNING: not removing hydrogen atom without neighbors 71% 1448/2039 [00:58<00:27, 21. 47it/s][21:10:18] WARNING: not removing hydrogen atom without neighbors 72% 1465/2039 [00:58<00:19, 29. 81it/s][21:10:19] WARNING: not removing hydrogen atom without neighbors 72% 1472/2039 [00:59<00:26, 21. 40it/s][21:10:19] WARNING: not removing hydrogen atom without neighbors 77% 1567/2039 [01:02<00:22, 21. 35it/s][21:10:22] WARNING: not removing hydrogen atom without neighbors 78% 1587/2039 [01:03<00:13, 33. 18it/s][21:10:23] WARNING: not removing hydrogen atom without neighbors 78% 1591/2039 [01:03<00:20, 21. 92it/s][21:10:23] WARNING: not removing hydrogen atom without neighbors 78% 1594/2039 [01:03<00:20, 21. 43it/s]WARNING: not removing hydrogen atom without neighbors 79% 1616/2039 [01:04<00:15, 26. 99it/s][21:10:24] WARNING: not removing hydrogen atom without neighbors 79% 1620/2039 [01:04<00:19, 21. 72it/s][21:10:24] WARNING: not removing hydrogen atom without neighbors 81% 1659/2039 [01:06<00:16, 23. 11it/s][21:10:26] WARNING: not removing hydrogen atom without neighbors 82% 1671/2039 [01:06<00:13, 28. 20it/s][21:10:26] WARNING: not removing hydrogen atom without neighbors 83% 1686/2039 [01:07<00:14, 23. 67it/s][21:10:27] WARNING: not removing hydrogen atom without neighbors [21:10:27] WARNING: not removing hydrogen atom without neighbors 83% 1689/2039 [01:07<00:17, 19. 68it/s][21:10:27] WARNING: not removing hydrogen atom without neighbors 84% 1712/2039 [01:08<00:14, 22. 21it/s][21:10:28] WARNING: not removing hydrogen atom without neighbors 85% 1736/2039 [01:09<00:09, 31. 18it/s][21:10:29] WARNING: not removing hydrogen atom without neighbors 87% 1766/2039 [01:10<00:08, 32. 87it/s][21:10:30] WARNING: not removing hydrogen atom without neighbors [21:10:30] WARNING: not removing hydrogen atom without neighbors 88% 1797/2039 [01:11<00:08, 29. 30it/s][21:10:31] WARNING: not removing hydrogen atom without neighbors 92% 1870/2039 [01:14<00:06, 26. 35it/s][21:10:34] WARNING: not removing hydrogen atom without neighbors 93% 1896/2039 [01:15<00:05, 26. 27it/s][21:10:35] WARNING: not removing hydrogen atom without neighbors 96% 1948/2039 [01:17<00:03, 27. 22it/s][21:10:37] WARNING: not removing hydrogen atom without neighbors 97% 1971/2039 [01:18<00:02, 26. 46it/s][21:10:38] WARNING: not removing hydrogen atom without neighbors 100% 2039/2039 [01:20<00:00, 25. 38it/s]

deepchem.pdf

Predicting output with the finetuned model ! python main. py predict --data_path exampledata / finetune / bbbp. csv \ --features_path exampledata / finetune / bbbp. npz \ --checkpoint_dir . / model \ --no_features_scaling \ --output data_pre. csv WARNING:root:No normalization for BCUT2D_MWHI WARNING:root:No normalization for BCUT2D_MWLOW WARNING:root:No normalization for BCUT2D_CHGHI WARNING:root:No normalization for BCUT2D_CHGLO WARNING:root:No normalization for BCUT2D_LOGPHI WARNING:root:No normalization for BCUT2D_LOGPLOW WARNING:root:No normalization for BCUT2D_MRHI WARNING:root:No normalization for BCUT2D_MRLOW [WARNING] Horovod cannot be imported; multi-GPU training is unsupported Loading training args Loading data Validating SMILES Test size = 2,039 Predicting... 0% 0/3 [00:00<?, ?it/s]Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. act_func. weig ht". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. edge_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. edge_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_q. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_k. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. heads. 0. mpn_v. W_h. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. act_func. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. layernorm. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_i. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 0. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 1. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. linear_layers. 2. bias". Loading pretrained parameter "grover. encoders. node_blocks. 0. attn. output_linear. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. W_o. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. weight". Loading pretrained parameter "grover. encoders. node_blocks. 0. sublayer. norm. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight".

deepchem.pdf

Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Loading pretrained parameter "readout. cached_zero_vector". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. weight". 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W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_atom. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_atom_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_atom. W_2. weight". 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Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". 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Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_1. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. weight". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. W_2. bias". Loading pretrained parameter "grover. encoders. ffn_bond_from_bond. act_func. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. atom_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_atom_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. weight". Loading pretrained parameter "grover. encoders. bond_from_bond_sublayer. norm. bias". Loading pretrained parameter "grover. encoders. act_func_node. weight". Loading pretrained parameter "grover. encoders. act_func_edge. weight". Loading pretrained parameter "readout. cached_zero_vector". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_atom_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_atom_ffn. 4. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 1. bias". Loading pretrained parameter "mol_atom_from_bond_ffn. 2. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. weight". Loading pretrained parameter "mol_atom_from_bond_ffn. 4. bias". Moving model to cuda 100% 3/3 [00:18<00:00, 6. 29s/it] Saving predictions to data_pre. csv Output The output will be saved in a file called data_pre. csv. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Github This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!

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An Introduction to PROTACs David Zhang and David Figueroa PROTACs represent an emerging wave of new therapeutics capable of modulating proteins once thought nearly impossible to target. By eliminating rather than merely inhibiting disease-causing proteins, PROTACs address the limitations of existing drug modalities. As PROTACs progress through clinical trials with promising results, there is growing anticipation surrounding their therapeutic potential. This Deep Chem tutorial serves as a starting point for exploring the world of PROTACs and the exciting field of targeted protein degradation. The tutorial is divided into five partitions: 1. Background literature 2. Data extraction 3. Featurization 4. Model deployment 5. References With that in mind, let's jump into how we can predict efficacy of PROTAC degraders! 1. Background literature Traditional drug modalities, such as small-molecule drugs or monoclonal antibodies, are limited to certain modes of action, like targeting specific receptors or blocking particular pathways. Targeted protein degradation (TPD) represents a promising new approach to modulate proteins that have been traditionally difficult to target. TPD has given rise to major classes of molecules that have emerged as promising therapeutic approaches against various disease contexts. 1. 1 Targeted protein degradation Targeted protein degradation represents a way of leveraging the cell's natural degradation mechanisms to target disease causing proteins. Typically, the cell maintains protein homeostasis through clearance by proteasomes or lysosomes. By leveraging these intrinsic cellular mechanisms, TPD methods can target a variety of proteins throughout the cell. This has given rise to a collection of TPD methods aimed at degrading proteins that may play roles across many disease states. One of these major class of molecules is proteolysis-targeting chimera (PROTACs). 1. 2 How do PROTACs work? PROTAC molecules are ternary structures consisting of a linker, a ligand to recruit and bind to the target protein, and a ligand to recruit the E3 ubiquitin ligase. Before we dive into how PROTACs mediate this degradation mechanism, it is crucial to understand the underlying biological pathway that makes this all possible. Figure 1: Molecular structure of PROTACs molecules designed to inhibit epidermal growth factor receptor (EGFR). The

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PROTAC linker connects the EGFR ligand and E3 ligase which are highlighted in yellow and gray, respectively [1]. 1. 1. 1 Ubiquitin Proteasome System The ubiquitin proteasome system, or UPS for short, is a crucial cellular maintenance mechanism. Ubiquitin-dependent proteolysis is a three-step process which involves ubiquitin-activating enzymes (E1), ubiquitin-conjugate enzymes (E2), and ubiquitin-protein ligases (E3). In general, E1 activates ubiquitin, priming it for transfer to E2 which interacts with E3 at which point E3 ligases are responsible for binding of the target protein substrate for subsequent ubiquitination by E2. Once the protein is tagged with a polyubiquitin chain, it is recognized by the proteasome, a large protease complex that degrades that protein into peptides. Figure 2: The ubiquitin proteasome system is one of the cell's internal degradation mechanism crucial for targetting dysfunctional proteins. Naturally this opens up opportunities to leverage this in a therapeutic context [2]. 1. 1. 2 Connection to PROTACs The realization that the UPS could be leveraged for therapeutic purposes was initially made through early studies of viruses and plants. The underlying idea involves design of small molecules capable of recruiting the E3 ligase and inducing degradation of a protein of interest (POI). This general idea naturally extended itself to the case of PROTACs. Together, the POI ligand, linker, and E3 ligase ligand make up the PROTAC complex responsible for protein degradation. Note that the presence of two ligands enables simultaneous recruitment of the E3 ligase and POI, hence its heterobifunctionality property. Furthermore, after the POI is degraded by the proteasome, PROTACs can disassociate and continue to induce further degradation, enabling low concentrations to be efficacious. This catalytic mechanism of action and event-drive pharmacology prevents PROTACs from suffering the same limitations as conventional therapeutic strategies such as drug resistance and off-target effects. Figure 3: The mechanism of action of PROTACs center around the UPS. In a heterobifunctional manner, recruiting both a target protein of interest and an E3 ligase, PROTACs are able to promote protein degradation in diseases [3].

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1. 3 Molecular Glues It is worth noting that PROTACs are not the only TPD method. Another major class which which also leverages the UPS to elicit degradation are molecular glues. As implied by its name, molecular glues stabilize protein-protein interactions between the target protein and E3 ligase. Notice how this is different than PROTACs which consists of two separate binding ligands connected by a linker. Consequently, molecular glues may be less sterically hindered without the need for a linker. However, identifying binding sites to induce new protein-protein interactions is typically harder than accomodating for existing ligands, as in the case for PROTACs, making it harder to design molecular glues. Figure 4: Lenalidomide is a molecular glue which mediates the interaction between CRBN, an E3 ligase, and CK1α, resulting in subsequent ubiquitination. [4] 1. 4 How can we leverage machine learning? As a novel and promising technique, PROTACs have demonstrated positive clinical results thus far. However, much of the clinical validation has been against classically drugged targets. In order for PROTACs to reach their full potential, their clinical efficacy against novel or hard to reach targets must be demonstrated. Consequently, there has been growing research in designing PROTAC molecules capable of elicting an effective response. However, much of the current work is empirical and requires extensive trial-and-error processes. Machine learning could potentially revolutionize this. By correlating molecular structure with physiochemical properties and biological activity, we could potentially streamline the discovery process, significantly reducing the time and cost associated with validation. With that in mind, let's jump into this tutorial to predict efficacy of PROTAC degraders! For a more in-depth dive into PROTACs, ubiquitin proteasome system, and targeted protein degradation, readers are referred to [5] and [6]. 2. Data extraction Before we proceed, let's install deepchem into our colab environment. ! pip install deepchem

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Collecting deepchem Downloading deepchem-2. 8. 0-py3-none-any. whl (1. 0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. 0/1. 0 MB 5. 8 MB/s eta 0:00:00 Requirement already satisfied: joblib in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 4. 2) Requirement already satisfied: numpy>=1. 21 in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 25. 2) Requirement already satisfied: pandas in /usr/local/lib/python3. 10/dist-packages (from deepchem) (2. 0. 3) Requirement already satisfied: scikit-learn in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 2. 2) Requirement already satisfied: sympy in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 12. 1) Requirement already satisfied: scipy>=1. 10. 1 in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 11. 4) Collecting rdkit (from deepchem) Downloading rdkit-2023. 9. 6-cp310-cp310-manylinux_2_17_x86_64. manylinux2014_x86_64. whl (34. 9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 34. 9/34. 9 MB 12. 0 MB/s eta 0:00:00 Requirement already satisfied: python-dateutil>=2. 8. 2 in /usr/local/lib/python3. 10/dist-packages (from pandas->d eepchem) (2. 8. 2) Requirement already satisfied: pytz>=2020. 1 in /usr/local/lib/python3. 10/dist-packages (from pandas->deepchem) ( 2023. 4) Requirement already satisfied: tzdata>=2022. 1 in /usr/local/lib/python3. 10/dist-packages (from pandas->deepchem) (2024. 1) Requirement already satisfied: Pillow in /usr/local/lib/python3. 10/dist-packages (from rdkit->deepchem) (9. 4. 0) Requirement already satisfied: threadpoolctl>=2. 0. 0 in /usr/local/lib/python3. 10/dist-packages (from scikit-lear n->deepchem) (3. 5. 0) Requirement already satisfied: mpmath<1. 4. 0,>=1. 1. 0 in /usr/local/lib/python3. 10/dist-packages (from sympy->deep chem) (1. 3. 0) Requirement already satisfied: six>=1. 5 in /usr/local/lib/python3. 10/dist-packages (from python-dateutil>=2. 8. 2->pandas->deepchem) (1. 16. 0) Installing collected packages: rdkit, deepchem Successfully installed deepchem-2. 8. 0 rdkit-2023. 9. 6 Now let's download this dataset on PROTACs, curated by [7], which includes 3270 PROTACs. # Python library imports import os # Deep Chem and scientific library imports import deepchem as dc import rdkit from rdkit import Chem from rdkit. Chem import Draw # Third-party library imports import pandas as pd import numpy as np import matplotlib. pyplot as plt os. system ( 'wget https://deepchemdata. s3. us-west-1. amazonaws. com/datasets/protac_10_06_24. csv' ) 0 protac_db = pd. read_csv ( 'protac_10_06_24. csv' ) Note that there exists a many-to-many mapping between PROTAC compounds and target proteins. A single PROTAC compound can be designed to target multiple proteins, and conversely, multiple PROTAC compounds can be developed to target the same protein. This many-to-many relationship allows for greater flexibility and adaptability in the design and application of PROTACs. print ( '''In this dataset, there are {} unique PROTAC compounds, targeting {} unique proteins for a total of {} combinations''' len ( protac_db [ 'Target' ]. unique ()), protac_db In this dataset, there are 3270 unique PROTAC compounds, targeting 323 unique proteins for a total of 5388 combi nations

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Compound ID Uniprot Target E3 ligase PDB Name Smiles DC50 (n M) 0 1 Q9NPI1 BRD7 VHL Na N Na N COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN... Na N 1 1 Q9H8M2 BRD9 VHL Na N Na N COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN... Na N 2 2 Q9NPI1 BRD7 VHL Na N Na N COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN... Na N 3 2 Q9H8M2 BRD9 VHL Na N Na N COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN... Na N 4 3 Q9H8M2 BRD9 CRBN Na N Na N COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN... Na N........................... 5383 3266 O60885 BRD4 FEM1B Na N Na N CC1=C(C)C2=C(S1)N1C(C)=NN=C1[C@H](CC(=O)NCCOCC... 1600 5384 3267 Na N BCR-ABL FEM1B Na N Na N CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(... Na N 5385 3268 Na N BCR-ABL FEM1B Na N Na N CC1=NC(NC2=NC=C(C(=O)NC3=C(C)C=CC=C3Cl)S2)=CC(... Na N 5386 3269 P03372 ER CRBN Na N ARV-471 O=C1CC[C@H] (N2CC3=CC(N4CCN(CC5CCN(C6=CC=C([C@@... 2 5387 3270 P10275 AR CRBN Na N ARV-110 N#CC1=CC=C(O[C@H]2CC[C@H](NC(=O)C3=CC=C(N4CCC(... 1 5388 rows × 89 columns Taking a closer look at the dataset, each PROTAC compound has a SMILEs representation along with its target protein of interest and E3 ligase. For reference, here is an example: example = protac_db. iloc [ 0 ] print ( '''Here is the SMILEs representation of a PROTAC compound: {} designed to target {} protein through ubiquitination by {} E3 ligase. '''. format ( example [ 'Smiles' ], example [ 'Target' Here is the SMILEs representation of a PROTAC compound: COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN1CCN(CCOCC OCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCC2=CC=C(C3=C(C)N=CS3)C=C2)C(C)(C)C)CC1 designed to target BRD7 protein through ubiquitination by VHL E3 ligase. protac_db. columns

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Index(['Compound ID', 'Uniprot', 'Target', 'E3 ligase', 'PDB', 'Name', 'Smiles', 'DC50 (n M)', 'Dmax (%)', 'Assay (DC50/Dmax)', 'Percent degradation (%)', 'Assay (Percent degradation)', 'IC50 (n M, Protac to Target)', 'Assay (Protac to Target, IC50)', 'EC50 (n M, Protac to Target)', 'Assay (Protac to Target, EC50)', 'Kd (n M, Protac to Target)', 'Assay (Protac to Target, Kd)', 'Ki (n M, Protac to Target)', 'Assay (Protac to Target, Ki)', 'delta G (kcal/mol, Protac to Target)', 'delta H (kcal/mol, Protac to Target)', '-T*delta S (kcal/mol, Protac to Target)', 'Assay (Protac to Target, G/H/-TS)', 'kon (1/Ms, Protac to Target)', 'koff (1/s, Protac to Target)', 't1/2 (s, Protac to Target)', 'Assay (Protac to Target, kon/koff/t1/2)', 'IC50 (n M, Protac to E3)', 'Assay (Protac to E3, IC50)', 'EC50 (n M, Protac to E3)', 'Assay (Protac to E3, EC50)', 'Kd (n M, Protac to E3)', 'Assay (Protac to E3, Kd)', 'Ki (n M, Protac to E3)', 'Assay (Protac to E3, Ki)', 'delta G (kcal/mol, Protac to E3)', 'delta H (kcal/mol, Protac to E3)', '-T*delta S (kcal/mol, Protac to E3)', 'Assay (Protac to E3, G/H/-TS)', 'kon (1/Ms, Protac to E3)', 'koff (1/s, Protac to E3)', 't1/2 (s, Protac to E3)', 'Assay (Protac to E3, kon/koff/t1/2)', 'IC50 (n M, Ternary complex)', 'Assay (Ternary complex, IC50)', 'EC50 (n M, Ternary complex)', 'Assay (Ternary complex, EC50)', 'Kd (n M, Ternary complex)', 'Assay (Ternary complex, Kd)', 'Ki (n M, Ternary complex)', 'Assay (Ternary complex, Ki)', 'delta G (kcal/mol, Ternary complex)', 'delta H (kcal/mol, Ternary complex)', '-T*delta S (kcal/mol, Ternary complex)', 'Assay (Ternary complex, G/H/-TS)', 'kon (1/Ms, Ternary complex)', 'koff (1/s, Ternary complex)', 't1/2 (s, Ternary complex)', 'Assay (Ternary complex, kon/koff/t1/2)', 'IC50 (n M, Cellular activities)', 'Assay (Cellular activities, IC50)', 'EC50 (n M, Cellular activities)', 'Assay (Cellular activities, EC50)', 'GI50 (n M, Cellular activities)', 'Assay (Cellular activities, GI50)', 'ED50 (n M, Cellular activities)', 'Assay (Cellular activities, ED50)', 'GR50 (n M, Cellular activities)', 'Assay (Cellular activities, GR50)', 'PAMPA Papp (nm/s, Permeability)', 'Assay (Permeability, PAMPA Papp)', 'Caco-2 A2B Papp (nm/s, Permeability)', 'Assay (Permeability, Caco-2 A2B Papp)', 'Caco-2 B2A Papp (nm/s, Permeability)', 'Assay (Permeability, Caco-2 B2A Papp)', 'Article DOI', 'Molecular Weight', 'Exact Mass', 'XLog P3', 'Heavy Atom Count', 'Ring Count', 'Hydrogen Bond Acceptor Count', 'Hydrogen Bond Donor Count', 'Rotatable Bond Count', 'Topological Polar Surface Area', 'Molecular Formula', 'In Ch I', 'In Ch I Key'], dtype='object') In general, the PROTAC-DB dataset contains information for a variety of different physiochemical and biochemical properties of PROTAC structures. Several useful ones to point out are, which describes the spontaneity of a chemical reaction, which measures the concentration of a ligand to achieve 50% occupancy of the protein binding sites, and which measures a compound's solubility, an indication of its absorption and distribution characteristics. Before we proceed, let's plot the distribution of each of these properties to get a better sense of our PROTAC dataset starting with ΔG values. delta_G = protac_db [ 'delta G (kcal/mol, Protac to E3)' ] delta_G = delta_G. dropna () delta_G = delta_G. astype ( float ) plt. hist ( delta_G, bins = 10 ) plt. xlabel ( 'ΔG (kcal/mol)' ) plt. ylabel ( 'Frequency' ) plt. title ( f 'Distribution of ΔG across PROTAC molecules' ) plt. plot () []

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Let's take a closer look at the distribution of PROTAC molecules around the -10 range of ΔG values. delta_G = protac_db [ 'delta G (kcal/mol, Protac to E3)' ] delta_G = delta_G. dropna () delta_G = delta_G. astype ( float ) x_min = -15 x_max = -5 bin_size = 1 bins = np. arange ( x_min, x_max, bin_size ) plt. hist ( delta_G, bins = bins ) plt. xlabel ( 'ΔG (kcal/mol)' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of ΔG ranged from -15 to -5 across PROTAC molecules' ) plt. plot () []

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There does not appear to be a lot of information on the spontaneity of PROTAC reactions but it is worth noting that the ones with recorded ΔGs appear energetically favorable, as expected. Let's now take a look at the values. kd_data = protac_db [ 'Kd (n M, Ternary complex)' ] kd_data = kd_data. dropna () kd_data = kd_data [ ~ kd_data. str. contains ( '/' )] kd_data = kd_data. astype ( float ) plt. hist ( kd_data ) plt. xlabel ( 'Dissociation constant (n M)' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of Kd values across PROTAC molecules' ) plt. plot () []

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Similar to ΔG values, there does not appear to be a lot of information on the affinity of formed PROTAC complexes. Since the range is so large, let's plot a second histogram focused on the PROTACs with low. kd_data = protac_db [ 'Kd (n M, Ternary complex)' ] kd_data = kd_data. dropna () kd_data = kd_data [ ~ kd_data. str. contains ( '/' )] kd_data = kd_data. astype ( float ) # limit range x_max = 1500 x_min = 0 bin_size = 25 bins = np. arange ( x_min, x_max, bin_size ) plt. hist ( kd_data, bins = bins ) plt. xlabel ( 'Dissociation constant (n M)' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of Kd values ranged from 0-1500 across PROTAC molecules' ) plt. plot () []

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The improved resolution of values illustrates a much cleaner distribution of values. We do see that a number of them have low, favorable values indicating that the PROTAC linker can form a strong connection with the E3 ligase and target protein. Let's now take a look at XLog P3 values. Note that this is slightly different than the typical Log P partition coefficient. Recall that Log P is defined In other words, Log P is the measured ratio of the concentration of a compound in the organic phase to the its concentration in the aqueous phase, measuring the compound's solubility. XLog P3 is a knowledge-based method for calculating the partition coefficient by accounting for the molecular structure, presence of functional groups, and bonding [8]. Both properties estimate a compound's liphophilicity, giving insight into how a compound may behave in biological systems. plt. hist ( protac_db [ 'XLog P3' ]) plt. xlabel ( 'XLog P3 Values' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of XLog P3 values across PROTAC molecules' ) plt. plot () []

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All PROTAC compounds have a recorded XLog P3 value. The distribution looks normally distributed with few molecules with extreme log P profiles. Now, let's take a look at the PROTAC degradation properties. "DC50 (n M)" and "Dmax (%)" represent the half maximal degradation concentration and maximal degradation of the target protein of interest, respectively. Let's take a quick look at their distributions. Let's first do some data cleaning # Let's first drop all the Na N values raw_dc50 = protac_db [ 'DC50 (n M)' ] raw_dc50 = raw_dc50. dropna () Notice that the values are all in string format with non-numerical characters such as '<', '/', and '>'. For the time being, let's remove these values. raw_dc50 = raw_dc50 [ ~ raw_dc50. str. contains ( '<|>|/|~|-' )] raw_dc50 = raw_dc50. astype ( float ) plt. hist ( raw_dc50. values, bins = 75 ) plt. xlabel ( 'PROTACs' ) plt. ylabel ( 'DC50 (n M)' ) plt. title ( 'DC50 for all PROTACs' ) plt. plot () []

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The distribution is certainly skewed and has a few outliers. Let's log normalize. lognorm_dc50 = np. log ( raw_dc50 ) plt. hist ( lognorm_dc50, bins = 15 ) plt. xlabel ( 'Log normalized DC50 values (log n M)' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of log normalized DC50 values' ) plt. plot () []

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Now, let's take a look at Dmax percentage which represents the maximal degradation a PROTAC can elicit relative to the total activity of the target protein of interest [7]. # Using the same row indices as our cleaned DC50 data dmax = protac_db. iloc [ lognorm_dc50. index ][ 'Dmax (%)' ] # Following the same data cleaning procedure: dmax = dmax. dropna () dmax = dmax [ ~ dmax. str. contains ( '<|>|/|~|-' )] dmax = dmax. astype ( float ) plt. hist ( dmax. values, bins = 10 ) plt. xlabel ( 'Dmax (%)' ) plt. ylabel ( 'Frequency' ) plt. title ( 'Distribution of Dmax (%)' ) plt. plot () []

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Notice that Dmax is represented as a percentage. For now, let's continue with regressing on DC50. We are now ready to featurize! # Let's predict DC50 properties for the time being cleaned_data = protac_db. iloc [ lognorm_dc50. index ] print ( 'There are {} PROTAC samples. '. format ( cleaned_data. shape [ 0 ])) There are 599 PROTAC samples. protac_smiles = cleaned_data [ 'Smiles' ] dc_vals = lognorm_dc50 3. Featurization Let's featurize using Circular Fingerprint which is incorporated in Deep Chem! Circular Fingerprint is a common featurizer for molecules that encode local information about each atom and their neighborhood. For more information, the reader is refered to [9]. from rdkit import Chem featurizer = dc. feat. Circular Fingerprint ( radius = 4, chiral = True ) features = featurizer. featurize ( protac_smiles ) # Let's initialize our dataset and perform splits dataset = dc. data. Numpy Dataset ( X = features, y = dc_vals, ids = list ( protac_smiles )) splitter = dc. splits. Random Splitter () train_random, val_random, test_random = splitter. train_valid_test_split ( dataset, seed = 42 ) Along with a random split, let's also use a scaffold split which ensures that the split contain a structurally diverse array of compounds. Scaffold split groups molecules according to presence of rings, linkers, combinations of rings and linkers, as well as atomic properties. In general, scaffold splits are a good way of ensuring generalizability of our models. # Scaffold split splitter = dc. splits. Scaffold Splitter () train_scaffold, val_scaffold, test_scaffold = splitter. train_valid_test_split ( dataset, seed = 42 ) To see the scaffold split in action, let's visualize the chosen compounds across the splits.

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print ( "Three molecules from the train set:" ) np. random. seed ( 42 ) indices = np. random. choice ( len ( train_scaffold ), size = 3, replace = False ) smiles = [ train_scaffold. ids [ index ] for index in indices ] mols = [ Chem. Mol From Smiles ( smile ) for smile in smiles ] Draw. Mols To Grid Image ( mols, mols Per Row = 3, sub Img Size = ( 450, 350 )) Three molecules from the train set: print ( "Three molecules from the validation set:" ) indices = np. random. choice ( len ( val_scaffold ), size = 3, replace = False ) smiles = [ val_scaffold. ids [ index ] for index in indices ] mols = [ Chem. Mol From Smiles ( smile ) for smile in smiles ] Draw. Mols To Grid Image ( mols, mols Per Row = 3, sub Img Size = ( 450, 350 )) Three molecules from the validation set: print ( "Five molecules from the test set:" ) indices = np. random. choice ( len ( test_scaffold ), size = 3, replace = False ) smiles = [ test_scaffold. ids [ index ] for index in indices ] mols = [ Chem. Mol From Smiles ( smile ) for smile in smiles ] Draw. Mols To Grid Image ( mols, mols Per Row = 3, sub Img Size = ( 450, 350 )) Five molecules from the test set: There are certainly functional group differences spread throughout the splits. Notice the presence of the nitrile group in the train set, amine group in the validation set, as well as the sulfonamide group in the test set. Additionally, notice the structural and conformational differences among the various data splits. It will be interesting to see how well our model generalizes. 4. Model deployment We have successfully generated our train and test datasets. Let's now create a simple MLP model to predict PROTAC degradation properties!

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# Initialize a 2 layer FCN import torch import torch. nn as nn n_tasks = 1 n_features = train_random. X. shape [ 1 ] layer_sizes = [ 256, 32, 1 ] dropouts = [ 0. 0, 0. 2, 0 ] activation_fns = [ nn. Re LU (), nn. Re LU (), nn. Identity ()] optimizer = dc. models. optimizers. Adam () # Let's log every train epoch batch_size = 10 log_freq = int ( len ( train_random ) / batch_size + 1 ) # L2 loss is default protac_model_random = dc. models. Multitask Regressor ( n_tasks, n_features, layer_sizes, dropouts = dropouts, activation_fns optimizer = optimizer, batch_size = 10, log_frequency = log_freq ) protac_model_scaffold = dc. models. Multitask Regressor ( n_tasks, n_features, layer_sizes, dropouts = dropouts, activation_fns optimizer = optimizer, batch_size = 10, log_frequency = log_freq ) Let's now wrap everything together to instantiate a Deep Chem model! Note that due to the small sample size, a smaller batch size actually helps performance. # protac_model = dc. models. torch_models. Torch Model(protac_model, loss=criterion, optimizer=optimizer, batch_size=10, log_frequency=log_freq) param_count = sum ( p. numel () for p in protac_model_random. model. parameters () if p. requires_grad ) print ( "There are {} trainable parameters". format ( param_count )) protac_model_random. model There are 532805 trainable parameters Pytorch Impl( (layers): Module List( (0): Linear(in_features=2048, out_features=256, bias=True) (1): Linear(in_features=256, out_features=32, bias=True) (2): Linear(in_features=32, out_features=1, bias=True) ) (output_layer): Linear(in_features=1, out_features=1, bias=True) (uncertainty_layer): Linear(in_features=1, out_features=1, bias=True) ) Let's define the validation function to prevent overfitting. train_losses_random = [] val_losses_random = [] train_losses_scaffold = [] val_losses_scaffold = [] metric = [ dc. metrics. Metric ( dc. metrics. mean_squared_error )] n_epochs = 100 for i in range ( n_epochs ): protac_model_random. fit ( train_random, nb_epoch = 1, all_losses = train_losses_random ) protac_model_scaffold. fit ( train_scaffold, nb_epoch = 1, all_losses = train_losses_scaffold ) # Validate on every other epoch if i % 2 == 0 : loss = protac_model_random. evaluate ( val_random, metrics = metric ) val_losses_random. append ( loss [ 'mean_squared_error' ]) loss = protac_model_scaffold. evaluate ( val_scaffold, metrics = metric ) val_losses_scaffold. append ( loss [ 'mean_squared_error' ]) We can easily look at how the training went through plotting the recorded losses. train_steps = [( i + 1 ) * log_freq for i in range ( len ( train_losses_random ))] val_steps = [( i + 1 ) * ( log_freq * 2 ) for i in range ( len ( val_losses_random ))] fig, ax = plt. subplots ( 1, 2, figsize = ( 12, 5 )) ax [ 0 ]. plot ( train_steps, train_losses_random, label = 'Train loss' ) ax [ 0 ]. plot ( val_steps, val_losses_random, label = 'Val loss' ) ax [ 0 ]. legend () ax [ 0 ]. set_xlabel ( 'Frequency of Steps' ) ax [ 0 ]. set_ylabel ( 'Loss' ) ax [ 0 ]. set_title ( 'Loss across train and validation random split' ) ax [ 1 ]. plot ( train_steps, train_losses_scaffold, label = 'Train loss' ) ax [ 1 ]. plot ( val_steps, val_losses_scaffold, label = 'Val loss' )

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ax [ 1 ]. legend () ax [ 1 ]. set_xlabel ( 'Frequency of Steps' ) ax [ 1 ]. set_ylabel ( 'Loss' ) ax [ 1 ]. set_title ( 'Loss across train and validation scaffold split' ) plt. plot () [] We can see that the model performs less well on the scaffold validation set which makes sense as the scaffold splits ensures that more validation molecules are out of distribution relative to the train distribution. Let's now perform some inference on our test set to evaluate our models! metrics = [ dc. metrics. Metric ( dc. metrics. mean_squared_error ), dc. metrics. Metric ( dc. metrics. pearsonr ), dc. metrics. Metric eval_metrics = protac_model_random. evaluate ( test_random, metrics ) preds = protac_model_random. predict ( test_random ) for k, v in eval_metrics. items (): print ( ' {} : {} '. format ( k, v )) mean_squared_error: 3. 074001339280645 pearsonr: 0. 818568671566446 pearson_r2_score: 0. 6700546700700561 import seaborn as sns preds_and_labels = np. concatenate (( test_random. y. reshape ([ 60, 1 ]), preds. reshape ([ 60, 1 ])), axis = 1 ) pred_df = pd. Data Frame ( preds_and_labels, columns = [ 'Actual log DC50 values', 'Predicted log DC50 values' ]) sns. jointplot ( pred_df, x = 'Predicted log DC50 values', y = 'Actual log DC50 values' ) plt. annotate ( f "R: { eval_metrics [ 'pearsonr' ] :. 2f } \n R²: { eval_metrics [ 'pearson_r2_score' ] :. 2f } ", xy = ( 0. 05, 0. 95 ), xycoords = 'axes fraction', ha = 'left', va = 'top', fontsize = 12, bbox = dict ( boxstyle = 'round,pad=0. 5', edgecolor = 'black', facecolor = 'white' )) # Set the title plt. suptitle ( 'Predicted vs Actual log DC50 Values from Random Split' ) # Adjust the position of the title to avoid overlap with the plot plt. tight_layout () plt. show ()

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The random split appears to do fairly well. Let's see how well our model does on the scaffold split. metrics = [ dc. metrics. Metric ( dc. metrics. mean_squared_error ), dc. metrics. Metric ( dc. metrics. pearsonr ), dc. metrics. Metric eval_metrics = protac_model_scaffold. evaluate ( test_scaffold, metrics ) preds = protac_model_scaffold. predict ( test_scaffold ) for k, v in eval_metrics. items (): print ( ' {} : {} '. format ( k, v )) mean_squared_error: 5. 991774828135091 pearsonr: -0. 10286796793151554 pearson_r2_score: 0. 010581818826359309 import seaborn as sns preds_and_labels = np. concatenate (( test_scaffold. y. reshape ([ 60, 1 ]), preds. reshape ([ 60, 1 ])), axis = 1 ) pred_df = pd. Data Frame ( preds_and_labels, columns = [ 'Actual log DC50 values', 'Predicted log DC50 values' ]) sns. jointplot ( pred_df, x = 'Predicted log DC50 values', y = 'Actual log DC50 values' ) plt. annotate ( f "R: { eval_metrics [ 'pearsonr' ] :. 2f } \n R²: { eval_metrics [ 'pearson_r2_score' ] :. 2f } ", xy = ( 0. 05, 0. 95 ), xycoords = 'axes fraction', ha = 'left', va = 'top', fontsize = 12, bbox = dict ( boxstyle = 'round,pad=0. 5', edgecolor = 'black', facecolor = 'white' )) # Set the title plt. suptitle ( 'Predicted vs Actual log DC50 Values from Scaffold Split' ) # Adjust the position of the title to avoid overlap with the plot plt. tight_layout () plt. show ()

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The model does significantly worse on the held out scaffold test set which was expected given the simplicity of the model. Developing far more complex models which can generalize out of distribution is a key area of focus in many areas of research from molecule property prediction to computer vision to natural language processing. In general, I hope this tutorial was a informative introduction into the world of PROTACs. Follow along as we explore how we can think about PROTAC design in the next tutorial! 5. References [1] Kelm, J. M., Pandey, D. S., Malin, E. et al. PROTAC'ing oncoproteins: targeted protein degradation for cancer therapy. Mol Cancer. 2023, 22, 62. https://doi. org/10. 1186/s12943-022-01707-5 [2] Tu, Y., Chen, C., Pan, J., Xu, J., Zhou, Z. G., & Wang, C. Y. The Ubiquitin Proteasome Pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. International journal of clinical and experimental pathology. 2012, 5, 8. [3] Sun, X., Gao, H., Yang, Y. et al. PROTACs: great opportunities for academia and industry. Sig Transduct Target Ther. 2019, 4, 64. https://doi. org/10. 1038/s41392-019-0101-6 [4] Che Y, Gilbert AM, Shanmugasundaram V, Noe MC. Inducing protein-protein interactions with molecular glues. Bioorg Med Chem Lett. 2018, 28, 15. https://doi. org/10. 1016/j. bmcl. 2018. 04. 046. [5] Békés, M., Langley, D. R. & Crews, C. M. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov. 2022, 21, 181-200. https://doi. org/10. 1038/s41573-021-00371-6 [6] Liu, Z., Hu, M., Yang, Y. et al. An overview of PROTACs: a promising drug discovery paradigm. Mol Biomed. 2022, 3 (46). https://doi. org/10. 1186/s43556-022-00112-0 [7] Gaoqi Weng, Xuanyan Cai, Dongsheng Cao, Hongyan Du, Chao Shen, Yafeng Deng, Qiaojun He, Bo Yang, Dan Li, Tingjun Hou, PROTAC-DB 2. 0: an updated database of PROTACs, Nucleic Acids Research. 2023, 51 (D1), Pages D1367-D1372, https://doi. org/10. 1093/nar/gkac946 [8] Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L. Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model. 2007, 47 (6), 2140-8. https://doi. org/10. 1021/ci700257y. [9] Glem RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs. 2006, 9 (3).

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Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!

deepchem.pdf

Protein Deep Learning by David Ricardo Figueroa Blanco In this tutorial we will compare protein sequences featurization such as one hot encoders and aminoacids composition. We will use some tools of Deep Chem and additional packages to create a model to predict melting temperature of proteins ( a good measurement of protein stability ) The melting temperature (MT) of a protein is a measurement of protein stability. This measure could vary from a big variety of experimental conditions, however, curated databases cand be found in literature https://aip. scitation. org/doi/10. 1063/1. 4947493. In this paper we can find a lot of thermodynamic information of proteins and therefore a big resource for the study of protein stability. Other information related with protein stability could be the change in Gibbs Free Energy due to a mutation. The study of protein stability is important in areas such as protein engineering and biocatalysis because catalytic efficiency could be directly related to the tertiary structure of the protein in study. Setup To run Deep Chem within Colab, you'll need to run the following installation commands. This will take about 5 minutes to run to completion and install your environment. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install Anaconda on your local machine. ! curl -Lo conda_installer. py https://raw. githubusercontent. com/deepchem/deepchem/master/scripts/colab_install. py import conda_installer conda_installer. install () ! /root/miniconda/bin/conda info -e ! pip install --pre deepchem ! pip install propy3 Data extraction In this cell, we download the dataset published in the paper https://aip. scitation. org/doi/10. 1063/1. 4947493 from the Deep Chem dataset repository import deepchem as dc import os from deepchem. utils import download_url data_dir = dc. utils. get_data_dir () download_url ( "https://deepchemdata. s3-us-west-1. amazonaws. com/datasets/pucci-proteins-appendixtable1. csv", dest_dir = print ( 'Dataset Dowloaded at {} '. format ( data_dir )) dataset_file = os. path. join ( data_dir, "pucci-proteins-appendixtable1. csv" ) Dataset Dowloaded at /tmp A closer look of the dataset: Contains the PDBid and the respective mutation and change in thermodynamical properties in each studied protein import pandas as pd data = pd. read_csv ( dataset_file ) data

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Unnamed: 0 N PDBid Chain RESN RESwt RESmut ΔTmexp Tmexp [wt] ΔΔHmexp... ΔΔGexp(T) T Nres 0 Na N 1 1aky A 8 VAL ILE-1. 5 47. 6 70... 5. 0 25 220 1 Na N 2 1aky A 48 GLN GLU-1. 3 47. 6 60... 4. 0 25 220 2 Na N 3 1aky A 77 THR HIS-1. 1 47. 6 130... 9. 0 25 220 3 Na N 4 1aky A 110 THR HIS-4. 8 47. 6 165... 11. 0 25 220 4 Na N 5 1aky A 169 ASN ASP-0. 6 47. 6 140... 9. 0 25 220............................................. 1621 Na N 1622 5pti_m52l A 15 LYS SER-1. 3 91. 7-5... 1. 2 25 58 1622 Na N 1623 5pti_m52l A 15 LYS THR-1. 1 91. 7-9...-3. 6 25 58 1623 Na N 1624 5pti_m52l A 15 LYS VAL-6. 3 91. 7 4... 4. 7 25 58 1624 Na N 1625 5pti_m52l A 15 LYS TRP-7. 5 91. 7 17... 8. 5 25 58 1625 Na N 1626 5pti_m52l A 15 LYS TYR-6. 6 91. 7 4... 4. 6 25 58 1626 rows × 23 columns Here we extract a small Data Frame that only contains a unique PDBid code and its respective melting temperature WT_Tm = data [[ 'PDBid', 'Tmexp [wt]' ]] WT_Tm. set_index ( 'PDBid', inplace = True ) WT_Tm Tmexp [wt] PDBid 1aky 47. 6 1aky 47. 6 1aky 47. 6 1aky 47. 6 1aky 47. 6...... 5pti_m52l 91. 7 5pti_m52l 91. 7 5pti_m52l 91. 7 5pti_m52l 91. 7 5pti_m52l 91. 7 1626 rows × 1 columns Here we create a dictionary that contains as keys, the pdbid of each protein and as values, the wild type melting temperature dict_WT_TM = {} for k, v in WT_Tm. itertuples (): if ( k not in dict_WT_TM ): dict_WT_TM [ k ] = float ( v ) Here we extract proteins with mutations reported only in chain A pdbs = data [ data [ 'PDBid' ]. str. len () < 5 ] pdbs = pdbs [ pdbs [ 'Chain' ] == "A" ] pdbs [[ 'RESN', 'RESwt', 'RESmut' ]]

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RESN RESwt RESmut 0 8 VAL ILE 1 48 GLN GLU 2 77 THR HIS 3 110 THR HIS 4 169 ASN ASP............ 1604 36 GLY ALA 1605 36 GLY SER 1606 37 GLY ALA 1607 39 ARG ALA 1608 46 LYS ALA 1509 rows × 3 columns This cell extracts the total number of mutations and changes in MT. In addition, we use a dicctionary to convert the residue mutation in a one letter code. alls = [] for resnum, wt in pdbs [[ 'RESN', 'RESwt', 'RESmut', 'PDBid', 'ΔTmexp' ]]. iteritems (): alls. append ( wt. values ) d = { 'CYS' : 'C', 'ASP' : 'D', 'SER' : 'S', 'GLN' : 'Q', 'LYS' : 'K', 'ILE' : 'I', 'PRO' : 'P', 'THR' : 'T', 'PHE' : 'F', 'ASN' : 'N', 'GLY' : 'G', 'HIS' : 'H', 'LEU' : 'L', 'ARG' : 'R', 'TRP' : 'W', 'ALA' : 'A', 'VAL' : 'V', 'GLU' : 'E', 'TYR' : 'Y', 'MET' : 'M' } resnum = alls [ 0 ] wt = [ d [ x. strip ()] for x in alls [ 1 ]] # extract the Wildtype aminoacid with one letter code mut = [ d [ x. strip ()] for x in alls [ 2 ]] # extract the Mutation aminoacid with one letter code codes = alls [ 3 ] # PDB code tms = alls [ 4 ] # Melting temperature print ( "pdbid {}, WT-AA {}, Resnum {}, MUT-AA {},Delta Tm {} ". format ( codes [ 0 ], wt [ 0 ], resnum [ 0 ], mut [ 0 ], tms [ 0 ])) pdbid 1aky, WT-AA V, Resnum 8, MUT-AA I,Delta Tm -1. 5 PDB Download Here we download all the pdbs by PDBID using the pdbfixer tool from pdbfixer import PDBFixer from simtk. openmm. app import PDBFile ! mkdir PDBs Using the fixer from pdbfixer we download each protein from its PDB code and fix some common problems present in the Protein Data Bank Files. This process will take around 15 minutes and 100 Mb. The use of the PDBFixer can be found in https://htmlpreview. github. io/?https://github. com/openmm/pdbfixer/blob/master/Manual. html . In our case, we download the pdb file from the pdb code and perform some curation such as find Nonstandar or missing residues, fix missing atoms import os import time t0 = time. time () downloaded = os. listdir ( "PDBs" ) PDBs_ids = set ( pdbs [ 'PDBid' ]) pdb_list = [] print ( "Start Download " ) for pdbid in PDBs_ids : name = pdbid + ". pdb" if ( name in downloaded ): continue try : fixer = PDBFixer ( pdbid = pdbid ) fixer. find Missing Residues () fixer. find Nonstandard Residues () fixer. replace Nonstandard Residues () fixer. remove Heterogens ( True )

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fixer. find Missing Atoms () fixer. add Missing Atoms () PDBFile. write File ( fixer. topology, fixer. positions, open ( '. /PDBs/ %s. pdb' % ( pdbid ), 'w' ), keep Ids = True ) except : print ( "Problem with {} ". format ( pdbid )) print ( "Total Time {} ". format ( time. time ()-t0 )) The following function help us to mutate a sequence denoted as A###B where A is the wildtype aminoacid, ### the position and, B the new aminoacid import re def Mutate Seq ( seq, Mutant ): ''' Mutate a sequence based on a string (Mutant) that has the notation : A###B where A is the wildtype aminoacid ### the position and B the mutation ''' aalist = re. findall ( '([A-Z])([0-9]+)([A-Z])', Mutant ) #(len(aalist)==1): newseq = seq listseq = list ( newseq ) for aas in aalist : wild AA = aas [ 0 ] pos = int ( aas [ 1 ]) -1 if ( pos >= len ( listseq )): print ( "Mutation not in the range of the protein" ) return None Mut AA = aas [-1 ] if ( listseq [ pos ] == wild AA ): listseq [ pos ] = Mut AA else : #print("Wild Type AA does not match") return None return ( "". join ( listseq )) The following function help us to identify a sequence of aminoacids base on PDB structures from Bio. PDB. PDBParser import PDBParser from Bio. PDB. Polypeptide import PPBuilder ppb = PPBuilder () def Get Seq From PDB ( pdbid ): structure = PDBParser (). get_structure ( pdbid. split ( ". " )[ 0 ], 'PDBs/ {} '. format ( pdbid )) seqs = [] return ppb. build_peptides ( structure ) Some examples of the described functions : Get Seq From PDB. Take one pdb that we previously downloaded and extract the sequence in one letter code import warnings ; warnings. simplefilter ( 'ignore' ) test = "1ezm" print ( test ) seq = Get Seq From PDB ( " {}. pdb". format ( test ))[ 0 ]. get_sequence () print ( "Original Sequence" ) print ( seq ) 1ezm Original Sequence AEAGGPGGNQKIGKYTYGSDYGPLIVNDRCEMDDGNVITVDMNSSTDDSKTTPFRFACPTNTYKQVNGAYSPLNDAHFFGGVVFKLYRDWFGTSPLTHKLYMKVHYGRSVEN AYWDGTAMLFGDGATMFYPLVSLDVAAHEVSHGFTEQNSGLIYRGQSGGMNEAFSDMAGEAAEFYMRGKNDFLIGYDIKKGSGALRYMDQPSRDGRSIDNASQYYNGIDVHH SSGVYNRAFYLLANSPGWDTRKAFEVFVDANRYYWTATSNYNSGACGVIRSAQNRNYSAADVTRAFSTVGVTCPSAL Information about the mutation inform Seq = Get Seq From PDB ( test + ". pdb" )[ 0 ]. __repr__ () print ( "Seq information", inform Seq ) start = re. findall ( '[0-9]+', inform Seq )[ 0 ] print ( "Reported Mutation {}{}{} ". format ( "R", 179, "A" )) numf = 179 - int ( start ) + 1 # fix some cases of negative aminoacid numbers Seq information <Polypeptide start=1 end=301> Reported Mutation R179A Mutation in the sequence. mutfinal = "R {} A". format ( numf ) print ( "Real Mutation = ", mutfinal ) mutseq = Mutate Seq ( seq, mutfinal )

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print ( mutseq ) Real Mutation = R179A AEAGGPGGNQKIGKYTYGSDYGPLIVNDRCEMDDGNVITVDMNSSTDDSKTTPFRFACPTNTYKQVNGAYSPLNDAHFFGGVVFKLYRDWFGTSPLTHKLYMKVHYGRSVEN AYWDGTAMLFGDGATMFYPLVSLDVAAHEVSHGFTEQNSGLIYRGQSGGMNEAFSDMAGEAAEFYMAGKNDFLIGYDIKKGSGALRYMDQPSRDGRSIDNASQYYNGIDVHH SSGVYNRAFYLLANSPGWDTRKAFEVFVDANRYYWTATSNYNSGACGVIRSAQNRNYSAADVTRAFSTVGVTCPSAL In this for loop we extract the sequences of all proteins in the dataset. In addition we created the mutated sequences and append the change in MT. In some cases, gaps in pdbs will cause that mutate Seq function fails, therefore this entries will be avoided. This is an important step in the whole process because creates a final tabulated data that contains the sequence and the Melting temperature ( our label) information = {} count = 1 failures = [] for code, tm, numr, wt_val, mut_val in zip ( codes, tms, resnum, wt, mut ): count += 1 seq = Get Seq From PDB ( " {}. pdb". format ( code ))[ 0 ]. get_sequence () mutfinal = "WT" if ( " {}-{} ". format ( code, mutfinal ) not in information ): inform Seq = Get Seq From PDB ( code + ". pdb" )[ 0 ]. __repr__ () start = re. findall ( '[-0-9]+', inform Seq )[ 0 ] if ( int ( start ) < 0 ): numf = numr - int ( start ) # if start is negative 0 is not used as resnumber else : numf = numr - int ( start ) + 1 mutfinal = " {}{}{} ". format ( wt_val, numf, mut_val ) mutseq = Mutate Seq ( seq, mutfinal ) if ( mutseq == None ): failures. append (( code, mutfinal )) continue information [ " {}-{} ". format ( code, mutfinal )] = [ mutseq, dict_WT_TM [ code ]-float ( tm )] Deep Learning and Machine Learning Models using proteins sequences import deepchem as dc import tensorflow as tf Here we extract two list, sequences (data) and melting temperature (label) seq_list = [] delta Tm = [] for i in information. values (): seq_list. append ( i [ 0 ]) delta Tm. append ( i [ 1 ]) max_seq = 0 for i in seq_list : if ( len ( i ) > max_seq ): max_seq = len ( i ) Here we use a One Hot Featurizer in order to convert protein sequences in numeric arrays codes = [ 'A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y' ] One Hot Featurizer = dc. feat. One Hot Featurizer ( codes, max_length = max_seq ) features = One Hot Featurizer. featurize ( seq_list ) Note that the One Hot Featurizer produces a matrix that contains the One Hot Vector for each sequence. features_vector = [] for i in range ( len ( features )): features_vector. append ( features [ i ]. flatten ()) dc_dataset = dc. data. Numpy Dataset ( X = features_vector, y = delta Tm ) dc_dataset <Numpy Dataset X. shape: (1497, 13188), y. shape: (1497,), w. shape: (1497,), task_names: [0]> Here we create a spliiter to perform a tran_test split of the dataset from deepchem import splits splitter = splits. Random Splitter () train, test = splitter. train_test_split ( dc_dataset, seed = 42 )

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Here we create a neuronal network using tensorflow-keras and using a loss function of "MAE" to evaluate the regression result. import tensorflow. keras as keras #from keras import layers model = tf. keras. Sequential ([ keras. layers. Dense ( units = 32, activation = 'relu', input_shape = dc_dataset. X. shape [ 1 :]), keras. layers. Dropout ( 0. 2 ), keras. layers. Dense ( units = 32, activation = 'relu' ), keras. layers. Dropout ( 0. 2 ), keras. layers. Dense ( units = 1 ), ]) model. compile ( loss = 'mae', optimizer = 'adam' ) print ( model. summary ()) history = model. fit ( train. X, train. y, validation_data = ( test. X, test. y ), batch_size = 100, epochs = 30, ) ## perform a plot of loss vs epochs import matplotlib. pyplot as plt history_df = pd. Data Frame ( history. history ) history_df [[ 'loss', 'val_loss' ]]. plot ()

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Model: "sequential_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_6 (Dense) (None, 32) 422048 _________________________________________________________________ dropout_4 (Dropout) (None, 32) 0 _________________________________________________________________ dense_7 (Dense) (None, 32) 1056 _________________________________________________________________ dropout_5 (Dropout) (None, 32) 0 _________________________________________________________________ dense_8 (Dense) (None, 1) 33 ================================================================= Total params: 423,137 Trainable params: 423,137 Non-trainable params: 0 _________________________________________________________________ None Epoch 1/30 12/12 [==============================] - 1s 20ms/step - loss: 62. 4284 - val_loss: 51. 3894 Epoch 2/30 12/12 [==============================] - 0s 10ms/step - loss: 45. 6483 - val_loss: 22. 2757 Epoch 3/30 12/12 [==============================] - 0s 12ms/step - loss: 19. 4803 - val_loss: 11. 7996 Epoch 4/30 12/12 [==============================] - 0s 11ms/step - loss: 18. 0858 - val_loss: 8. 6031 Epoch 5/30 12/12 [==============================] - 0s 11ms/step - loss: 14. 5904 - val_loss: 9. 4577 Epoch 6/30 12/12 [==============================] - 0s 10ms/step - loss: 14. 3444 - val_loss: 7. 3325 Epoch 7/30 12/12 [==============================] - 0s 10ms/step - loss: 13. 6787 - val_loss: 7. 5799 Epoch 8/30 12/12 [==============================] - 0s 10ms/step - loss: 14. 0674 - val_loss: 6. 6186 Epoch 9/30 12/12 [==============================] - 0s 11ms/step - loss: 12. 8215 - val_loss: 7. 4920 Epoch 10/30 12/12 [==============================] - 0s 11ms/step - loss: 13. 0748 - val_loss: 5. 4614 Epoch 11/30 12/12 [==============================] - 0s 11ms/step - loss: 12. 3646 - val_loss: 6. 7943 Epoch 12/30 12/12 [==============================] - 0s 10ms/step - loss: 11. 5250 - val_loss: 5. 2098 Epoch 13/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 5254 - val_loss: 5. 4262 Epoch 14/30 12/12 [==============================] - 0s 11ms/step - loss: 12. 1312 - val_loss: 6. 4607 Epoch 15/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 4129 - val_loss: 5. 6157 Epoch 16/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 4406 - val_loss: 6. 0425 Epoch 17/30 12/12 [==============================] - 0s 12ms/step - loss: 11. 4762 - val_loss: 5. 3330 Epoch 18/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 4820 - val_loss: 8. 5933 Epoch 19/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 1607 - val_loss: 5. 6460 Epoch 20/30 12/12 [==============================] - 0s 12ms/step - loss: 11. 2637 - val_loss: 5. 3362 Epoch 21/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 4390 - val_loss: 6. 2368 Epoch 22/30 12/12 [==============================] - 0s 10ms/step - loss: 10. 8070 - val_loss: 6. 2875 Epoch 23/30 12/12 [==============================] - 0s 11ms/step - loss: 11. 1277 - val_loss: 5. 3496 Epoch 24/30 12/12 [==============================] - 0s 10ms/step - loss: 11. 1626 - val_loss: 5. 0670 Epoch 25/30 12/12 [==============================] - 0s 10ms/step - loss: 10. 9386 - val_loss: 5. 0440 Epoch 26/30 12/12 [==============================] - 0s 14ms/step - loss: 11. 0402 - val_loss: 5. 6448 Epoch 27/30 12/12 [==============================] - 0s 14ms/step - loss: 10. 7278 - val_loss: 5. 3868 Epoch 28/30 12/12 [==============================] - 0s 13ms/step - loss: 10. 6073 - val_loss: 5. 4149 Epoch 29/30 12/12 [==============================] - 0s 12ms/step - loss: 11. 0812 - val_loss: 5. 9349 Epoch 30/30 12/12 [==============================] - 0s 14ms/step - loss: 10. 2336 - val_loss: 5. 7489 <Axes Subplot:>

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Deep Chem Keras Model Note that Deep Chem Keras model allows to create a deepchem model based on the previously built model of keras. All the information in the training can be access with model. model. history model_dc = dc. models. Keras Model ( model, dc. models. losses. L1Loss ()) model_dc. fit ( train ) 10. 399123382568359 History_df = pd. Data Frame ( model_dc. model. history. history ) History_df [[ 'loss', 'val_loss' ]]. plot () <Axes Subplot:> metric = dc. metrics. Metric ( dc. metrics. pearson_r2_score ) print ( 'test dataset R2:', model_dc. evaluate ( test, [ metric ])) test dataset R2: {'pearson_r2_score': 0. 7125054529591869} Examples of Classic ML models Finally, we will compare others descriptros such as AAcomposition and Composition,transition and distribution of AA ( https://www. pnas. org/content/92/19/8700 ) from propy import Py Pro In the following cell, we are creating and py Pro Object based on the protein sequence. Pypro allows us the calculation of amino acid composition vectors Here we create a list with the aminoacido composition vector for each sequence used in the previous model. import numpy as np aa Complist = [] CTDList = [] for seq in seq_list : Obj = Py Pro. Get Pro Des ( seq ) aa Complist. append ( np. array ( list ( Obj. Get AAComp (). values ()))) CTDList. append ( np. array ( list ( Obj. Get CTD (). values ()))) dc_dataset_aacomp = dc. data. Numpy Dataset ( X = aa Complist, y = delta Tm ) dc_dataset_ctd = dc. data. Numpy Dataset ( X = CTDList, y = delta Tm ) Evaluation of classical machine learning models

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In the following cell we create a random Forest Regressor and the deepchem Sklearn Model. As it was used in the DL models, here we use "MAE" score to evaluate the results of the regression from deepchem import splits splitter = splits. Random Splitter () train, test = splitter. train_test_split ( dc_dataset_aacomp, seed = 42 ) from sklearn. ensemble import Random Forest Regressor from deepchem. utils. evaluate import Evaluator import pandas as pd print ( "Random Forest Regressor" ) seed = 42 # Set a random seed to get stable results sklearn_model = Random Forest Regressor ( n_estimators = 100, max_features = 'sqrt' ) sklearn_model. random_state = seed model = dc. models. Sklearn Model ( sklearn_model ) model. fit ( train ) metric = dc. metrics. Metric ( dc. metrics. mae_score ) train_score = model. evaluate ( train, [ metric ]) test_score = model. evaluate ( test, [ metric ]) print ( "Train score is : {} ". format ( train_score )) print ( "Test score is : {} ". format ( test_score )) Random Forest Regressor Train score is : {'mae_score': 1. 7916551501995608} Test score is : {'mae_score': 3. 8967191996673947} In the following cell we create a Suport Vector Regressor and the deepchem Sklearn Model. As it was used in the DL models, here we use "MAE" score to evaluate the results of the regression print ( "Support Vector Machine Regressor" ) from sklearn. svm import SVR svr_sklearn = SVR ( kernel = "poly", degree = 4 ) svr_sklearn. random_state = seed model = dc. models. Sklearn Model ( svr_sklearn ) model. fit ( train ) metric = dc. metrics. Metric ( dc. metrics. mae_score ) train_score = model. evaluate ( train, [ metric ]) test_score = model. evaluate ( test, [ metric ]) print ( "Train score is : {} ". format ( train_score )) print ( "Test score is : {} ". format ( test_score )) Support Vector Machine Regressor Train score is : {'mae_score': 3. 275727325767219} Test score is : {'mae_score': 4. 058136267284038} Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!

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An Introduction to Antibody Design Using Protein Language Models By Dhuvi Karthikeyan and Aaron Menezes This Deep Chem tutorial is designed to serve as a brief primer for antibody design via protein language models. Antibodies are immune proteins also known as immunoglobulins that are naturally produced in the body and bind/inactivate viruses and other pathogens. They are a valuable therapeutic and save lives in immune checkpoint inhibitors for cancer and as neutralizing antibodies for acute viral infection. In addition, they are well known outside the hospital walls for their ability to bind and stick to arbitrary molecular targets, a useful feature in the basic sciences and industrial biochemical facilities. This tutorial aims to provide a quick overview of key immunology concepts needed to understand antibody structure and function in the broader context of the immune system. We make some assumptions with familiarity with large language models. Take a look at our other tuorial. For the sake of brevity we provide links on non-essential topics that point to external sources wherever possible. Follow along to learn more about the immune system, and protein language models for guided Ab design. Note: This tutorial is loosely based on the 2023 Nature Biotechnology paper titled "Efficient evolution of human antibodies from general protein language models" [1] by Hie et al. We thank the authors for making their methods and data available and accessible. 1. Immunology 101 1. 1 What is the immune system? Like other body systems such as the digestive system or the cardiovascular system, the immune system is fundamentally a collection of specialized cells that operate together to accomplish a specific homeostatic function. The immune system is responsible for the protection of our body's vast resources (lipids, carbodhydrates, enzymes, proteins), especially against opportunistic threats of the outside world, such as viruses and bacteria. A very simple yet powerful component of the immune system is the skin, which keeps what's out, out and what's in, in. However, what happens when we get a cut/scrape or when we walk past someone who's coughing and accidentally breathe in the droplets? Thankfully, it turns out the immune system has a whole army of highly specialized white blood cells patrolling our blood and lymph nodes, ready to launch a multi-layered response against these and the countless other cases where foreign objects may enter our bodies. Through these white bloods cells, the immune system is able to accomplish its primary function, self-nonself discrimination, the accurate identification of molecules that are sufficiently dissimilar than those derived from a healthy person. If you would like to learn more about the complex problem of self non-self discrimination, and appreciate the theory behind the immune system's organization and function, we recommend checking out the following works: A Theory of Self-Nonself Discrimination [2] The common sense of the self-nonself discrimination, 2005 [3] Conceptual aspects of self and nonself discrimination, 2011 [4] Self-Nonself Discrimination due to Immunological Nonlinearities: the Analysis of a Series of Models by Numerical Methods, 1987 [5] A biological context for the self-nonself discrimination and the regulation of effector class by the immune system, 2005 [6] 1. 2 Innate vs. Adaptive Immune System Remarkably, these white blood cells operate independently, adding and subtracting from the local context (tissue microenvironment), orchestrating cohesive responses in a completely decentralized manner. Individual white blood cells interact with their environments through general cytokine and chemokine surface receptors that facilitate gradient sensing of the local environment. This is the primary axis by which immune cells are able to home to sites of infection/wound healing and send pro-inflammatory or anti-inflammatory signals to neighboring cells. In addition, nearly all immune cells require some level of activation by means of receptor:ligand stimulation by non-self signatures. How specific these receptors are helps highlight the delineation between the two arms of the immune system: the innate and adaptive immune systems. 1. 2. 1 The Innate Immune System The innate immune system is the body's first line of defense, and encompasses different types of cells (and proteins) that recognize broadly non-self signals known as pathogen-associated or damage-associated molecular patterns ( PAMPs

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or DAMPs ). The effector cells of the innate immune system are decorated with ~20 different kinds of pattern recognition receptors (PRRs) that recognize broad signals (single stranded DNA, LPS, other signals of pathogen activity). Being the first responders, the innate immune system handles containment, engulfing the offending signals and otherwise blocking it off, and tries to destroy the threat. Lastly, these cells simultaneously sound the alarm, putting the local area into a heightened state of threat detection and infection prevention, such as spiking a fever and initiating swelling to draw in more immune cells. 1. 2. 2 The Adaptive Immune System The adaptive immune system on the other hand, is slow to respond and is often brought in by innate cell activation. Instead of broad PRRs, it relies on tens of millions of somatically rearranged and stochastically generated highly specific adaptive immune receptors (AIRs) whose shape complementarity allows them to identify their complementary molecular patterns called epitopes. There are many more differences between the innate and adaptive immune systems, such as the latter's ability to develop a durable memory response, and those are introduced in greater detail here [7]. It is important to note however, that it is through the independent and asynchronous operation of both the innate and adaptive immune system that we see the dynamics [8] of threat detection, message passing, calling for reinforcements, homing of adaptive immune cells, and activation/expansion of these cells, resulting in complete clearance of the pathogen. Another key distinction between the innate and adaptive immune system is the formation of a robust memory response. Amazingly, evolution has steered this system to imprint past pathogen exposures so that upon re-exposure to the same signal, a small pool of memory cells is activated and antigen-specifc adaptive immune cells rapidly proliferatre and clear the antigen-source [9]. algorithmic Adaptive Immune Algorithm 1. Nascent progenitor cells undergoes somatic recombination to stochastically generate an Adaptive Immune Receptor (AIR). 2. Self-selecting methods of ensuring self-tolerance remove cells that react too strongly to the self before being released into the blood. 3. In periphery, naive (antigen-inexperienced) cells interact with antigens via AIRs in search of their cognate epitope. 4. Upon recognition of a sufficiently strong epitope reaction, they divide rapidly and overwhelm the offending antigen with sheer numbers and specialized effector function. 5. After the threat has cleared, this expanded population contracts as cells die without activation signalling, and a lasting pool of memory cells remains. 6. Memory pool persists and is reactivated in an effector state upon antigen reintroduction. 1. 2. 3 Effector Cells of the Adaptive Immune System There are two major types of effector cells in the adaptive immune response: T-cells and B-cells. Both the T-cell and B-cell populations in the body are contextualized through their adaptive immune receptors, T-cell Receptors (TCRs) and B-cell Receptors (BCRs), respectively. We can think of both populations as repertoires of receptors, conferring protection against the threats recognized by the repertoire. T-cells help maitnain cellular immunity by interrogating the intracellular component of our bodies' by using their TCRs to scan recycled protein fragments presented at the cell surface (Read more about T-cell mediated immunity here [10] ). B-cells, on the other hand, use their BCRs to survey the extracellular compartment and are tasked with upkeeping humoral immunity : neutralizing threats floating around in the blood and plasma. As effector cells of the adaptive immune response, both T-cell and B-cells are similar in their development and their operation as a unit, though the specifics of per cell function are quite different. Note : A helpful distinction between antigens and epitopes is that an antigen is something that broadly generates an immune response and can have multiple epitopes. Epitopes are specific molecular patterns that have a matching paratope (binding surface of an adaptive immune receptor).

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Image Source: Creative Diagnostics 1. 3 B-Cells and Antibodies B-cells, or B-lymphocytes, get their name not from their origin in the bone marrow, but from their discovery in a particular organ of the chicken [11]. These cells circulate in the bloodstream, equipped with unique B-cell receptors (BCRs) that allow them to recognize specific antigens, leading to their activation. This process often requires additional stimulation from helper T-cells which provide essential co-stimulatory signals, an additional layer of verification for non-selfness. Over the course of the COVID pandemic, whether we wanted to or not were exposed to the concept of antibodies and learned of their association with some sort of protective capacity against SARS-COV-2. But what are they, and where do they originate from? Antibodies (Abs) are typically represented as Y-shaped proteins that bind to their cognate epitope surfaces with high specificity and affinity, similar to how TCRs and BCRs bind to their epitopes. This is because antibodies are the soluble form of the B-cell receptor that is secreted into the blood upon B-cell activation in the presence of its cognate antigen. The secretion of large amounts of antibodies is the primary effector function of B-cells. Upon activation, a B-cell will divide, with the daughter cells inheriting the same BCR, and some of these cells will differentiate into plasma cells, which are the Ab factories capable of secreting thousands of Abs/min. This is especially useful upon antigen re-encounter where a large amount of antibodies are released by memory cells which neutralize the pathogen even before we develop the symptoms of infection (this is what most common vaccines are designed to do). Image Source: Beckman Neutralizing mechanisms of pathogenesis is only one way that antibody tagging is useful to immune defense. Antibody tagging plays a key role in a number of humoral immunity processes: 1. Neutralization : De-activation of pathogenic function by near complete coating of the functional component of pathogens or toxins by antibodies to inhibit interaction with host cells (i. e. and antibody that binds to the surface glycoproteins on SARS-COV2 now inhibit that virus particle's ability to enter cells expressing ACE2). 2. Opsonization : Partial coating of pathogens enhances rates phagocytosis and removal from the blood by cells of the innate immune system. 3. Agglutination/Precipitation ): Since antibodies have 2 arms (each arm of the Y), they can cross-link and form anitbody-antigens chains which can precipitate out of the plasma and increase their chances of being recognized as aberrant and cleared by phagocytes. 4. Complement Activation : The complement system is a collection of inactive proteins and protein precursors are self-amplifying on activation and help with multiple aspects of humoral immunity. Yet another function of antibodies is their role in initiating the complement cascade that ends in the lysis or phagocytosis of pathogens.

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Image Source: The Immune System: Innate and Adaptive Body Defenses Figure 21. 15 pulled from [Source] Given the importance of B-cell mediated immunity, as operationalized by the body's antibody repertoire, it's clear that the diversity of BCR clones plays a critical role in our ability to mount an effective response against a pathogen. The maintenance of a robust BCR repertoire highlights not only the complexity of the immune response but also underscores the potential for leveraging the modularity of this mechanism to introduce new clones for their extraordinary precision in therapeutics such as vaccine development. 1. 4 Antibody Sequence, Structure, and Function The remarkable diversity of antibodies is achieved through somatic recombination, or gene rearrangement at the DNA level that occurs outside of meiosis. The AIR-specific somatic recombination is known as V(D)J recombination and generates both TCR and BCR diversity. During V(D)J recombination, a single gene per set is sampled from the set of variable (V), diversity (D), and joining (J) gene segments and randomly joined together with some baked in error (insertions) to create stable BCRs with unique antigen-binding sites. Additionally, B-cells have an additional process that further amplifies the diversity as well as the functional capacity of antigen-specific antibodies. This process is known as somatic hypermutation. When an activated B-cell divides, somatic hypermutation (SHM) introduces point mutations in the variable regions of BCR generating minor variants of each BCR. These daughter cells compete for survival signals mediated through antigen binding such that only the stronger binders survive. Structurally, antibodies are composed of two identical light chains and two identical heavy chains, linked by disulfide bonds. Each chain contributes to the formation of the antigen-binding site, located in the variable regions. Within these regions, hypervariable loops known as complementarity determining regions (CDRs) dictate the specificity and affinity of the antibody-antigen interaction. This specificity is measured in terms of affinity using the dissociation constant (Kd), and the avidity (affinity over multi-valent binding sites, see Ig M, Ig A ). The antibody molecule is divided into two main functional regions: 1. Fab Region (Fragment, antigen-binding): Contains the variable regions of the light and heavy chains, responsible for antigen recognition and binding. 2. Fc Region (Fragment, crystallizable): Composed of the constant regions of the heavy chains, mediates interactions with innate immune cells and the complement system.

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Image Source: Dianova: Antibody Structure \ By harnessing the selection of evolutionary pressures during somatic hypermutation, the B-cell compartment uses a powerful method of further tuning the antibody specificity to have some of the highest affinity interactions in the known protein universe [12]. Their high precision and binding affinities have caused their broad adoption in not only therapeutics but commercial and research applications as well as to tag proteins in solution in flow cytometry, Cy TOF, immuno-precipitation, and other target identification assays. 1. 5 Current Paradigms for Antibodies as a Therapeutic Modality Given their unparalleled ability to precisely and durably bind arbitrary targets, there has been a significant interest in possessing antibodies for desired targets. There are a number of therapeutic use cases for these antibodies, for diseases ranging from transplant rejection, non-Hodgkin's lymphoma, immune checkpoint inhibitors for cancer immunotherapy, psoriasis, multiple sclerosis, Crohn's disease, and many more. While antibodies against common pathogens can be isolated from the serum of convalescent individuals and screened for specificity, the process of procuring novel antibodies is substantially more challenging and involves inoculating an animal with an antigen and isolating the antibodies after. For example, anti-venom is a solution of antibodies derived from animals (typically horses) against cytotoxic proteins found in venom. This procedure is both resource and labor intensive. This is because after inoculation and isolation of antibodies from the animal, there is an additional step of screening them for reactivity against the target using reaction chemistry methods such as surface plamon resonance or bio-layer interferometry. As such there has been a great deal of interest in methods of in-silico antibody design. A number of approaches have shown reasonable degrees of success in this task from guided evolution based approaches [1] to newer diffusion based [13] approaches. In this tutorial we will pay homage to the former. 2. Let's Code! Designing Antibodies via Directed Evolution 2. 1 Overview Now that we have the minimal backgrounded needed to understand the antibody design proble and the necessary language model background, we can jump right into antibody design via directed evolution, as shown in the figure below: \

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Image Source: Figure 1. Outeiral et. al 2. 2 Setup/Methodology In Hie et al. the authors decide to use a general protein language model instead of one trained specifically on antibody sequences. They use the ESM-1b and ESM-1v models which were trained on Uni Ref50 and Uni Ref90 [14], respectively. For their directed evolution studies they select seven therapeutic antibodies associated with viral infection spanning Influenza, Ebolavirus, and SARS-COv2. The authors use a straightforward and exhaustive mutation scheduler in mutating every residue in the antigen binding region to every other residue and computing the likelihood of the sequence. Sequences with likelihoods greater than or equal to the WT sequence were kept for experimental validation. For our purposes, we need not be as thorough and can use a slightly expedited method by taking the top-k mutations at a specific point. Inspired by the work of Hie et al., we first define the p LM driven directed evolution task as simply passing in a masked antibody sequence to a p LM that was previously trained on the masked language modeling objective and examining the token probabilities for the masked amino acids. It really is that easy! For reference we break the task down into the following steps: # Antibody Design via p LM Directed Evolution 1. Select a pre-trained model language model (can be pre-trained on all domains or exclusively antibodies) 2. Choose an antibody to mutate. 3. Determine the amino acid(s) to mask out*. 4. Pass the tokenized sequences into the p LM 5. Sample tokens according to a heuristic for increased fitness *Modification of antibodies needs to focus only on the variable regions as the amino acids at the interface are the ones

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responsible for driving affinity. Making edits to the constant region would actually be detrimental to antibodies' effector function in the complement system as well as potentially disrupt binding to innate immune receptors. \ 2. 2. 1 Loading the Model + Tokenizer For this exploration, we pay homage to an early Antibody Language Model Ab Lang [15]. Ab Lang is a masked language model based on the Ro BERTa [16] model, and pre-trained on antibody sequences from the [observed antibody space (OAS) [17]. Ab Lang consists of two models, one trained on the heavy chain sequences and one trained on the light chain sequences and the authors demonstrate its usefulness over broader protein language models such as ESM-1b, contradicting the findings put forth in Hie et al. Both the heavy and light chain models are identical in architecture with a of 768, max position embedding of 160, and 12 transformer block layers, totaling ~86M parameters. from transformers import Auto Model, Auto Tokenizer, Auto Model For Masked LM # Get the tokenizer tokenizer = Auto Tokenizer. from_pretrained ( 'qilowoq/Ab Lang_light' ) # Get the light chain model mlm_light_chain_model = Auto Model For Masked LM. from_pretrained ( 'qilowoq/Ab Lang_light' ) # Get the heavy chain model mlm_heavy_chain_model = Auto Model For Masked LM. from_pretrained ( 'qilowoq/Ab Lang_heavy' ) /usr/local/lib/python3. 10/dist-packages/huggingface_hub/utils/_token. py:89: User Warning: The secret `HF_TOKEN` does not exist in your Colab secrets. To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface. co/settings/ tokens), set it as secret in your Google Colab and restart your session. You will be able to reuse this secret in all of your notebooks. Please note that authentication is recommended but still optional to access public models or datasets. warnings. warn( tokenizer_config. json: 0%| | 0. 00/367 [00:00<?, ?B/s] vocab. txt: 0%| | 0. 00/71. 0 [00:00<?, ?B/s] tokenizer. json: 0%| | 0. 00/3. 02k [00:00<?, ?B/s] special_tokens_map. json: 0%| | 0. 00/125 [00:00<?, ?B/s] config. json: 0%| | 0. 00/848 [00:00<?, ?B/s] pytorch_model. bin: 0%| | 0. 00/343M [00:00<?, ?B/s] config. json: 0%| | 0. 00/848 [00:00<?, ?B/s] pytorch_model. bin: 0%| | 0. 00/343M [00:00<?, ?B/s] # Lets take a look at the model parameter count and architecture n_params = sum ( p. numel () for p in mlm_heavy_chain_model. parameters ()) print ( f 'The Ablang model has { n_params } trainable parameters. \n ' ) mlm_heavy_chain_model The Ablang model has 85809432 trainable parameters.

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Roberta For Masked LM( (roberta): Roberta Model( (embeddings): Roberta Embeddings( (word_embeddings): Embedding(24, 768, padding_idx=21) (position_embeddings): Embedding(160, 768, padding_idx=21) (token_type_embeddings): Embedding(2, 768) (Layer Norm): Layer Norm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) (encoder): Roberta Encoder( (layer): Module List( (0-11): 12 x Roberta Layer( (attention): Roberta Attention( (self): Roberta Self Attention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0. 1, inplace=False) ) (output): Roberta Self Output( (dense): Linear(in_features=768, out_features=768, bias=True) (Layer Norm): Layer Norm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) (intermediate): Roberta Intermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): Roberta Output( (dense): Linear(in_features=3072, out_features=768, bias=True) (Layer Norm): Layer Norm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0. 1, inplace=False) ) ) ) ) ) (lm_head): Roberta LMHead( (dense): Linear(in_features=768, out_features=768, bias=True) (layer_norm): Layer Norm((768,), eps=1e-12, elementwise_affine=True) (decoder): Linear(in_features=768, out_features=24, bias=True) ) ) 2. 2. 2 Example Antibody Sequence Next let's choose a heavy chain and light chain for designing. These were chosen from the Ablang examples page on Hugging Face, # Lets take the variable regions of the heavy and light chains heavy_chain_example = 'EVQLQESGPGLVKPSETLSLTCTVSGGPINNAYWTWIRQPPGKGLEYLGYVYHTGVTNYNPSLKSRLTITIDTSRKQLSLSLKFVTAADSAVYYCAREWAEDGDFGNAFHVWGQGTMVAVSSASTKGPSVFPLAPSSKSTSGGTAALGCL' light_chain_example = 'GSELTQDPAVSVALGQTVRITCQGDSLRNYYASWYQQKPRQAPVLVFYGKNNRPSGIPDRFSGSSSGNTASLTISGAQAEDEADYYCNSRDSSSNHLVFGGGTKLTVLSQ' 2. 2. 3 Masking the Sequence One of the crucial parameters with this approach is in the determination of which residues to mask and re-design. Let's start off by first setting up some reproducible code so that we can apply the masking procedure to any number of sequences at arbitrary points. # Sequnece masking convenience function def mask_seq_pos ( sequence : str, idx : int, mask = '[MASK]' ): '''Given an arbitrary antibody sequence with and a seqeunce index, convert the residue at that index into the mask token. ''' cleaned_sequence = sequence. replace ( ' ', '' ) # Get ride of extraneous spaces if any assert abs ( idx ) < len ( sequence ), "Zero-indexed value needs to be less than sequence length minus one. " cleaned_sequence = list ( cleaned_sequence ) # Turn the sequence into a list cleaned_sequence [ idx ] = '*' # Mask the sequence at idx masked_sequence = ' '. join ( cleaned_sequence ) # Convert list -> seq masked_sequence = masked_sequence. replace ( '*', mask ) return masked_sequence # Test assert mask_seq_pos ( 'CAT', 1 ) == 'C [MASK] T'

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#TODO: Add unit tests with pytest where you can check that the assert has been hit 2. 2. 3 Model Inference ### Step 1. Mask the light_chain sequence mask_idx = 9 masked_light_chain = mask_seq_pos ( light_chain_example, idx = mask_idx ) ### Step 2. Tokenize tokenized_input = tokenizer ( masked_light_chain, return_tensors = 'pt' ) ### Step 3. Light Chain Model mlm_output = mlm_light_chain_model ( ** tokenized_input ) ### Step 4. Decode the outputs to see what the model has placed decoded_outs = tokenizer. decode ( mlm_output. logits. squeeze (). argmax ( dim = 1 ), skip_special_tokens = True ) print ( f 'Model predicted: { decoded_outs. replace ( " ", "" )[ 9 ] } at index { mask_idx } ' ) print ( f 'Predicted Sequence: { decoded_outs. replace ( " ", "" ) } ' ) print ( f 'Starting Sequence: { light_chain_example } ' ) Model predicted: S at index 9 Predicted Sequence: SADSSSCGVSSTVAHGQTLKINSQGQRHSLYYVRWYQQKPGLAPLLLIYGKNSRPSGIPDRFSGSKSGTTASLTITGLQAEDEADYYCQQSG GSGGHLTVGGGALLATLTQ Starting Sequence: GSELTQDPAVSVALGQTVRITCQGDSLRNYYASWYQQKPRQAPVLVFYGKNNRPSGIPDRFSGSSSGNTASLTISGAQAEDEADYYCNSRDS SSNHLVFGGGTKLTVLSQ 2. 2. 4 Hugging Face Pipeline Object Hold on, given the tokenized input with only one masked token, we would expect to see only one change to the the sequence. However, what we get back is something a lot more different that what we put in. Luckily, there's something in the Hugging Face software suite that we can use to address this: Pipelines Hugging Face Pipelines: 1. Pipeline object is a wrapper for inference and can be treated like an object for API calls 2. There is a fill-mask pipeline that we can use which accepts a single mask token in out input and outputs a dictionary of the score of that sequence, the imputed token, and the reconstructed full sequence. Lets see it in action: from transformers import pipeline filler = pipeline ( task = 'fill-mask', model = mlm_light_chain_model, tokenizer = tokenizer ) filler ( masked_light_chain ) # fill in the mask [{'score': 0. 13761496543884277, 'token': 7, 'token_str': 'S', 'sequence': 'G S E L T Q D P A S S V A L G Q T V R I T C Q G D S L R N Y Y A S W Y Q Q K P R Q A P V L V F Y G K N N R P S G I P D R F S G S S S G N T A S L T I S G A Q A E D E A D Y Y C N S R D S S S N H L V F G G G T K L T V L S Q'}, {'score': 0. 1152879148721695, 'token': 6, 'token_str': 'E', 'sequence': 'G S E L T Q D P A E S V A L G Q T V R I T C Q G D S L R N Y Y A S W Y Q Q K P R Q A P V L V F Y G K N N R P S G I P D R F S G S S S G N T A S L T I S G A Q A E D E A D Y Y C N S R D S S S N H L V F G G G T K L T V L S Q'}, {'score': 0. 0989701896905899, 'token': 9, 'token_str': 'N', 'sequence': 'G S E L T Q D P A N S V A L G Q T V R I T C Q G D S L R N Y Y A S W Y Q Q K P R Q A P V L V F Y G K N N R P S G I P D R F S G S S S G N T A S L T I S G A Q A E D E A D Y Y C N S R D S S S N H L V F G G G T K L T V L S Q'}, {'score': 0. 08586061000823975, 'token': 14, 'token_str': 'A', 'sequence': 'G S E L T Q D P A A S V A L G Q T V R I T C Q G D S L R N Y Y A S W Y Q Q K P R Q A P V L V F Y G K N N R P S G I P D R F S G S S S G N T A S L T I S G A Q A E D E A D Y Y C N S R D S S S N H L V F G G G T K L T V L S Q'}, {'score': 0. 07652082294225693, 'token': 8, 'token_str': 'T', 'sequence': 'G S E L T Q D P A T S V A L G Q T V R I T C Q G D S L R N Y Y A S W Y Q Q K P R Q A P V L V F Y G K N N R P S G I P D R F S G S S S G N T A S L T I S G A Q A E D E A D Y Y C N S R D S S S N H L V F G G G T K L T V L S Q'}] Congratulations you have now designed 5 new antibodies! Disclaimer: For a more thorough antibody (re)design, we will typically want to follow an approach like what was done in Hie et al. where every point along the sequence will be mutated and the total number of sequences will be collated and scored with the top-100 or so antibodies being expressed for validation. If you would like to explore this feel free to try it out yourself as a challenge!

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You can also refer to the real data in Hie et al. to see if any of the predicted ones were found to work well and increase fitness. 2. 3 Limitations While promising, this approach is obviously not without its shortcomings. Key limitations include: Fixed length antibody design since masked tokens are applied in a 1:1 fashion. Lack of target information included during conditional sampling step which can influence choice of amino acid given the sequence context. Approach is sensitive to choice of protein language model This letter [18] provides a great synopsis of Hie et al. 's work, which by extension apply to the methods presented in this tutorial as well. Citing this Tutorial If you found this tutorial useful, please consider citing it as: @manual{Bioinformatics, title={An Introduction to Antibody Design Using Protein Language Models}, organization={Deep Chem}, author={Karthikeyan, Dhuvarakesh and Menezes, Aaron}, howpublished = {\url{https://github. com/deepchem/deepchem/blob/master/examples/tutorials/Deep Chem_Antibody Tutorial_Simplified. ipynb}}, year={2024}, } Works Cited [1] Hie, B. L., Shanker, V. R., Xu, D. et al. Efficient evolution of human antibodies from general protein language models. Nat Biotechnol 42, 275-283 (2024). https://doi. org/10. 1038/s41587-023-01763-2 [2] Bretscher, P., & Cohn, M. (1970). A Theory of Self-Nonself Discrimination. Science, 169(3950), 1042-1049. doi:10. 1126/science. 169. 3950. 1042 [3] Cohn, M. The common sense of the self-nonself discrimination. Springer Semin Immun 27, 3-17 (2005). https://doi. org/10. 1007/s00281-005-0199-1 [4] Gonzalez S, González-Rodríguez AP, Suárez-Álvarez B, López-Soto A, Huergo-Zapico L, Lopez-Larrea C. Conceptual aspects of self and nonself discrimination. Self Nonself. 2011 Jan;2(1):19-25. doi: 10. 4161/self. 2. 1. 15094. Epub 2011 Jan 1. PMID: 21776331; PMCID: PMC3136900. [5] ROB J. DE BOER, PAULINE HOGEWEG, Self-Nonself Discrimination due to Immunological Nonlinearities: the Analysis of a Series of Models by Numerical Methods, Mathematical Medicine and Biology: A Journal of the IMA, Volume 4, Issue 1, 1987, Pages 1-32, https://doi. org/10. 1093/imammb/4. 1. 1 [6] Cohn, M. A biological context for the self-nonself discrimination and the regulation of effector class by the immune system. Immunol Res 31, 133-150 (2005). https://doi. org/10. 1385/IR:31:2:133 [7] Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science; 2001. Principles of innate and adaptive immunity. Available from: https://www. ncbi. nlm. nih. gov/books/NBK27090/ [8] Perelson, A. Modelling viral and immune system dynamics. Nat Rev Immunol. 2. , 28-36 (2002). https://doi. org/10. 1038/nri700 [9] Shittu, A. (n. d. ). Understanding immunological memory. ASM. org. https://asm. org/articles/2023/may/understanding-immunological-memory [10] Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science; 2001. Chapter 8, T Cell-Mediated Immunity. Available from: https://www. ncbi. nlm. nih. gov/books/NBK10762/ [11] Glick, B., Chang, T. S., & Jaap, R. G. (1956). The Bursa of Fabricius and Antibody Production. Poultry Science, 35(1), 224-225. doi:10. 3382/ps. 0350224 [12] Nooren, I. M. (2003). NEW EMBO MEMBER'S REVIEW: Diversity of protein-protein interactions. EMBO Journal, 22(14),

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3486-3492. https://doi. org/10. 1093/emboj/cdg359 [13] Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, & Andreas Loukas. (2024). Ab Diffuser: Full-Atom Generation of in vitro Functioning Antibodies. [14] Baris E. Suzek, Hongzhan Huang, Peter Mc Garvey, Raja Mazumder, Cathy H. Wu, Uni Ref: comprehensive and non-redundant Uni Prot reference clusters, Bioinformatics, Volume 23, Issue 10, May 2007, Pages 1282-1288, https://doi. org/10. 1093/bioinformatics/btm098 [15] Tobias H Olsen, Iain H Moal, Charlotte M Deane, Ab Lang: an antibody language model for completing antibody sequences, Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac046, https://doi. org/10. 1093/bioadv/vbac046 [16] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, & Veselin Stoyanov. (2019). Ro BERTa: A Robustly Optimized BERT Pretraining Approach. [17] Olsen TH, Boyles F, Deane CM. Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci. 2022 Jan;31(1):141-146. doi: 10. 1002/pro. 4205. Epub 2021 Oct 29. PMID: 34655133; PMCID: PMC8740823. [18] Outeiral, C., Deane, C. M. Perfecting antibodies with language models. Nat Biotechnol 42, 185-186 (2024). https://doi. org/10. 1038/s41587-023-01991-6

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Introduction to binding sites By Elisa Gómez de Lope Table of Contents: Introduction Basic concepts Types of binding sites Computational methods to study binding sites Deep Chem tools How does a binding pocket look like? Further Reading This tutorial can also be used in Google colab. If you'd like to open this notebook in colab, you can use the following link. This notebook is made to run without any GPU support. O p e n i n C o l a b O p e n i n C o l a b Introduction Binding sites are specific locations on a molecule where ligands, such as substrates, inhibitors, or other molecules, can attach through various types of molecular interactions. Binding sites are crucial for the function of many biological molecules. They are typically located on the surface of proteins or within their three-dimensional structure. When a ligand binds to a binding site, it can induce a conformational change in the protein, which can either activate or inhibit the protein's function. This binding process is essential for numerous biological processes, including enzyme catalysis, signal transduction, and molecular recognition. For example, in enzymes, the binding site where the substrate binds is often referred to as the active site. In receptors, the binding site for signaling molecules (such as hormones or neurotransmitters) is critical for transmitting signals inside the cell. Understanding binding sites is particularly relevant for the development of new drugs, as it can lead to the development of more effective and selective drugs from multiple angles: Target identification: Identifying binding sites on target proteins allows researchers to design molecules that can specifically interact with these sites, leading to the development of drugs that can modulate the protein's activity. Drug design: Knowledge of the structure and properties of binding sites enables the design of drugs with high specificity and affinity, reducing off-target effects and increasing efficacy. Optimization: Detailed understanding of binding interactions helps improving the binding characteristics of drug candidates, such as increasing binding affinity and selectivity. Additionally, knowledge of the structure and properties of binding sites enables the design of drugs with high specificity and affinity, reducing off-target effects and increasing efficacy.

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Myoglobin (blue) with its ligand heme (orange) bound. Based on PDB: 1MBO. Basic concepts Here we cover some basic notions to understand the science of binding site identification. Molecular Interactions The specific interactions that occur at the binding site can be of various types, including (non-exhaustive list): Hydrogen Bonding: Weak electrostatic interactions between hydrogen atoms bonded to highly electronegative atoms (like oxygen, nitrogen, or fluorine) and other electronegative atoms or functional groups. Hydrogen bonding is important for stabilizing protein-ligand complexes and can be enhanced by halogen bonding. Halogen Bonding: A type of intermolecular interaction where a halogen atom (like iodine or fluorine) acts as an acceptor, forming a bond with a hydrogen atom or a multiple bond. Halogen bonding can significantly enhance the affinity of ligands for binding sites. Orthogonal Multipolar Interactions: Interactions between backbone carbonyls, amide-containing side chains, guanidinium groups, and sulphur atoms, which can also enhance binding affinity. Van der Waals Forces: Weak, non-specific interactions arising from induced electrical interactions between closely approaching atoms or molecules. They usually provide additional stabilization and contribute to the overall binding affinity, especially in close-contact regions. Metal coordination: Interactions between metal ions (e. g., zinc, magnesium) and ligands that have lone pairs of electrons (e. g., histidine, cysteine, water). These interactions are typically coordinate covalent bonds, where both electrons in the bond come from the ligand, and are crucial in metalloenzymes and metalloproteins, where metal ions often play a key role in catalytic activity and structural stability. Polar Interactions: Interactions between polar functional groups, such as hydrogen bond donors (e. g., backbone NH, polarized Cα-H, polar side chains, and protein-bound water) and acceptors (e. g., backbone carbonyls, amide-containing side chains, and guanidinium groups). Hydrophobic Interactions: Non-polar interactions between lipophilic side chains, which can contribute to the binding

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affinity of ligands. Pi Interactions: Interactions between aromatic rings (Pi-Pi), and aromatic rings with other types of molecules (Halogen-Pi, Cation-Pi,... ). They occur in binding sites with aromatic residues such as phenylalanine, tyrosine, and tryptophan, and stabilize the binding complex through stacking interactions. Ligands Ligands are molecules that bind to specific sites on proteins or other molecules, facilitating various biological processes. Ligands can be classified into different types based on their function: Substrates: Molecules that bind to an enzyme's active site and undergo a chemical reaction. Inhibitors: Molecules that bind to an enzyme or receptor and block its activity. Activators: Molecules that bind to an enzyme or receptor and increase its activity. Cofactors: Non-protein molecules (metal ions, vitamins, or other small molecules) that bind to enzymes to modify their activity. They can act as activators or inhibitors depending on the specific enzyme and the binding site. Signaling Lipids: Lipid-based molecules that act as signaling molecules, such as steroid hormones. Neurotransmitters: Chemical messengers that transmit signals between neurons and their target cells. The binding of ligands to their target sites is influenced by various physicochemical properties:

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Size and Shape: Ligands must be the appropriate size and shape to fit into the binding site. Charge: Electrostatic interactions, such as ionic bonds, can contribute to ligand binding. Hydrophobicity: Hydrophobic interactions between non-polar regions of the ligand and the binding site can stabilize the complex. Hydrogen Bonding: Hydrogen bonds between the ligand and the binding site can also play a crucial role in binding affinity. The specific interactions between a ligand and its binding site, as well as the physicochemical properties of the ligand, are essential for understanding and predicting ligand-receptor binding events Binding Affinity and Specificity Binding affinity is the strength of the binding interaction between a biomolecule (e. g., a protein or DNA) and its ligand or binding partner (e. g., a drug or inhibitor). It is typically measured and reported by the equilibrium dissociation constant ( ), which is used to evaluate and rank the strengths of bimolecular interactions. The smaller the value, the greater the binding affinity of the ligand for its target. Conversely, the larger the value, the more weakly the target molecule and ligand are attracted to and bind to one another. Binding affinity is influenced by non-covalent intermolecular interactions, such as hydrogen bonding, electrostatic interactions, hydrophobic interactions, and van der Waals forces between the two molecules. The presence of other molecules can also affect the binding affinity between a ligand and its target. Binding specificity refers to the selectivity of a ligand for binding to a particular site or target. Highly specific ligands will bind tightly and selectively to their intended target, while less specific ligands may bind to multiple targets with varying affinities. It is determined by the complementarity between the ligand and the binding site, including factors such as size, shape, charge, and hydrophobicity (see section above on ligands). Specific interactions, like hydrogen bonding and ionic interactions, contribute to the selectivity of the binding. Binding specificity is crucial in various biological processes, such as enzymatic reactions or drug-target interactions, as it allows for specific and regulated interactions, which is essential for the proper functioning of biological systems. Antigen-antibody interactions are particularly highly specific binding sites (often also characterized by high affinity). The specificity of these interactions is fundamental to ensure precise immune recognition and response. In summary, binding affinity measures the strength of the interaction between a ligand and its target, while binding specificity determines the selectivity of the ligand for a particular binding site or target. Thermodynamics of Binding The thermodynamics of binding involves the interplay of enthalpy ( ), entropy ( ), and Gibbs free energy (

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) to describe the binding of ligands to binding sites. These thermodynamic parameters are crucial in understanding the binding process and the forces involved. Enthalpy ( ) is a measure of the total energy change during a process. In the context of binding, enthalpy represents the energy change associated with the formation of the ligand-binding site complex. A negative enthalpy change indicates that the binding process is exothermic, meaning that heat is released during binding. Conversely, a positive enthalpy change indicates an endothermic process, where heat is absorbed during binding. Entropy ( ) measures the disorder or randomness of a system. In binding, entropy represents the change in disorder associated with the formation of the ligand-binding site complex. A negative entropy change indicates a decrease in disorder, which is often associated with the formation of a more ordered complex. Conversely, a positive entropy change indicates an increase in disorder, which can be seen in the disruption of the binding site or the ligand. Gibbs free energy ( ) is a measure of the energy change during a process that takes into account both enthalpy and entropy. It is defined as, where is the temperature in Kelvin. Gibbs free energy represents the energy available for work during a process. In binding, a negative Gibbs free energy change indicates that the binding process is spontaneous and favorable, while a positive Gibbs free energy change indicates that the process is non-spontaneous and less favorable. Calculated variation of the Gibbs free energy (G), entropy (S), enthalpy (H), and heat capacity (Cp) of a given reaction plotted as a function of temperature (T). The solid curves correspond to a pressure of 1 bar; The dashed curve shows variation in ∆G at 8 GPa. Binding isotherms are models that describe the relationship between the concentration of ligand and the occupancy of binding sites. These isotherms are crucial in understanding the binding process and the forces involved. One example is the Langmuir isotherm, which assumes that the binding site is homogeneous and the ligand binds to the site with a single binding constant. Cooperative binding occurs when the binding of one ligand molecule affects the binding of subsequent ligand molecules. This can lead to non-linear binding isotherms, where the binding of ligands is enhanced or inhibited by the presence of other ligands. Cooperative binding is often seen in systems where multiple binding sites are involved or where the binding site is heterogeneous. Kinetics of Binding The kinetics of binding involves the study of the rates at which ligands bind to and dissociate from binding sites. Rate constants for association and dissociation are essential in describing the kinetics of binding. They represent the rate at which the ligand binds or dissociates to the site, respectively. Kinetic models are used to describe the binding process. One commonly used kinetic model to describe enzyme kinetics is the Michaelis-Menten model, which assumes that the enzyme has a single binding site and that the binding of the substrate is reversible. There are other kinetic models, including the Langmuir adsorption model.

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Binding curves for three ligands following the Hill-Langmuir model, each with a (equilibrium dissociation constant) of 10 µM for its target protein. The blue ligand shows negative cooperativity of binding, meaning that binding of the first ligand reduces the binding affinity of the remaining site(s) for binding of a second ligand. The red ligand shows positive cooperativity of binding, meaning that binding of the first ligand increases the binding affinity of the remaining site(s) for binding of a second ligand. Types of binding sites Binding sites can be classified based on various criteria depending on the context. This is a non-exhaustive list of binding sites categories based on the type of molecule they bind to: Protein Binding Sites: These are regions on a protein where other molecules can bind. They can be further divided into: Active Sites: Regions where enzymes bind substrates and catalyze chemical reactions. Example: The active site of the enzyme hexokinase binds to glucose and ATP, catalyzing the phosphorylation of glucose. Allosteric Sites: Regions where ligands bind and alter the protein's activity without being part of the active site. Example: The binding of 2,3-bisphosphoglycerate (2,3-BPG) to hemoglobin enhances the ability of hemoglobin to release oxygen where it is most needed. Regulatory Sites: Regions where ligands bind and regulate protein activity or localization. Example: Binding of a regulatory protein to a specific site on a receptor can modulate the receptor's activity. Nucleic Acid Binding Sites: These are regions on DNA or RNA where other molecules can bind. They can be further divided into: Transcription Factor Binding Sites: Regions where transcription factors bind to regulate gene expression. Example: The TATA box is a DNA sequence that transcription factors bind to initiate transcription. Restriction Sites: Regions where restriction enzymes bind to cleave DNA. Example: The Eco RI restriction enzyme recognizes and cuts the DNA sequence GAATTC. Recombination Sites: Regions where site-specific recombinases bind to facilitate genetic recombination. Example: The lox P sites are recognized by the Cre recombinase enzyme to mediate recombination. Small Molecule Binding Sites: These are regions on proteins or nucleic acids where small molecules like drugs or substrates bind. They can be further divided into: Compound Binding Sites: Regions typically located at the active site of the enzyme where the substrate binds and undergoes a chemical reaction, usually reversible. Example: The binding site for the drug aspirin on the enzyme cyclooxygenase (COX) inhibits its activity. Cofactor Binding Sites: Regions where cofactors can bind, sometimes permanently and covalently attached to the protein, and can be located at various sites. Example: The binding site for the heme cofactor in hemoglobin, which is essential for oxygen transport. Ion and Water Binding Sites: These are regions on proteins or nucleic acids where ions or water molecules bind. The calcium-binding sites in calmodulin, which are crucial for its role in signal transduction. Computational methods to study binding sites Multiple computational methods have been developed in the last decades to study and identify binding sites, offering insights that complement experimental approaches. These methods enable the prediction of binding affinities, the

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identification of potential drug candidates, and the understanding of the dynamic nature of binding interactions. Here are some key computational approaches used to study binding sites: Method Description Applications Tools Output Molecular Docking Predicts the preferred orientation of a ligand when bound to a protein or nucleic acid binding site. Used extensively in drug discovery to screen large libraries of compounds and identify potential drug candidates. Auto Dock, Glide, DOCK Binding poses, binding affinities, interaction maps Molecular Dynamics (MD) Simulations Provides a dynamic view of the binding process by simulating the physical movements of atoms and molecules over time. Used to study the stability of ligand binding, conformational changes, and the effect of mutations on binding affinity. GROMACS, AMBER, CHARMM Trajectories of atomic positions, binding free energies, insights into the flexibility and dynamics of binding sites Quantum Mechanics/Molecular Mechanics (QM/MM) Methods Combines quantum mechanical calculations for the active site with molecular mechanical calculations for the rest of the system. Used to study reaction mechanisms, electronic properties, and the role of metal ions in binding sites. Gaussian, ORCA, Q-Chem Detailed electronic structure information, reaction pathways, energy profiles Homology Modeling Predicts the 3D structure of a protein based on the known structure of a homologous protein. Useful for studying binding sites in proteins for which no experimental structure is available. MODELLER, SWISS-MODEL, Phyre2 Predicted 3D structures that can be used for docking and MD simulations Virtual Screening Computationally screens large libraries of compounds to identify potential ligands that bind to a target binding site. Widely used in drug discovery to prioritize compounds for experimental testing. Schrodinger's Glide, Auto Dock Vina, GOLD Ranked lists of compounds with predicted binding affinities and poses Machine Learning and AI Uses machine learning and AI techniques to predict binding affinities, identify binding sites, and generate new ligand structures. Enhancing the accuracy of docking predictions, predicting drug-target interactions, and designing novel compounds. Deep Chem, Tensor Flow, Py Torch, protein language models (ESM2) Predictive models, binding affinity predictions, novel ligand designs Protein-Ligand Interaction Fingerprints (PLIF) Computational representations of the interactions between a protein and a ligand. Used to compare binding modes, identify key interactions, and cluster similar binding sites. MOE, Schrödinger's Maestro Interaction maps, similarity scores Recently, large language models, particularly protein language models (PLMs), have emerged as powerful tools for predicting protein properties. These models typically use a transformer-based architecture to process protein sequences, learning relationships between amino acids and protein properties. PLMs can then be fine-tuned for specific tasks, such as binding site prediction, reducing the need for large, specific training datasets and offering high scalability. Deep Chem tools for studying binding sites Deep Chem in particular has a few tools and capabilities for identifying binding pockets on proteins: Binding Pocket Finder : This is an abstract superclass in Deep Chem that provides a template for child classes to algorithmically locate potential binding pockets on proteins. The idea is to help identify regions of the protein that may be good interaction sites for ligands or other molecules. Convex Hull Pocket Finder : This is a specific implementation of the Binding Pocket Finder class that uses the convex hull of the protein structure to find potential binding pockets. It takes in a protein structure and returns a list of binding pockets represented as Coordinate Boxes. Pose generators : Pose generation is the task of finding a “pose”, that is a geometric configuration of a small molecule interacting with a protein. A key step in computing the binding free energy of two complexes is to find low energy “poses”, that is energetically favorable conformations of molecules with respect to each other. This can be useful for identifying favorable binding modes and orientations (low energy poses) of ligands within a protein's binding site. Current implementations allow for Autodock Vina and GNINA. Docking : There is a generic docking implementation that depends on provide pose generation and pose scoring utilities to perform docking. There is a tutorial on using machine learning and molecular docking methods to predict the binding energy of a protein-ligand complex, and another tutorial on using atomic convolutions in particular to model such interactions. How does a binding site look like? Let's visualize a binding pocket in greater detail. For this purpose, we need to download the structure of a protein that contains a binding site. As an illustrative example, we show here a well-known protein-ligand pair: the binding of the

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drug Imatinib (Gleevec) to the Abl kinase domain of the BCR-Abl fusion protein, which is commonly associated with chronic myeloid leukemia (CML). The PDB ID for this complex is '1IEP'. # setup ! pip install py3Dmol biopython requests import requests import py3Dmol from Bio. PDB import PDBParser, Neighbor Search from io import String IO Requirement already satisfied: py3Dmol in /usr/local/lib/python3. 10/dist-packages (2. 1. 0) Requirement already satisfied: biopython in /usr/local/lib/python3. 10/dist-packages (1. 83) Requirement already satisfied: requests in /usr/local/lib/python3. 10/dist-packages (2. 31. 0) Requirement already satisfied: numpy in /usr/local/lib/python3. 10/dist-packages (from biopython) (1. 25. 2) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3. 10/dist-packages (from request s) (3. 3. 2) Requirement already satisfied: idna<4,>=2. 5 in /usr/local/lib/python3. 10/dist-packages (from requests) (3. 7) Requirement already satisfied: urllib3<3,>=1. 21. 1 in /usr/local/lib/python3. 10/dist-packages (from requests) (2. 0. 7) Requirement already satisfied: certifi>=2017. 4. 17 in /usr/local/lib/python3. 10/dist-packages (from requests) (20 24. 6. 2) # Download the protein-ligand complex (PDB file) pdb_id = "1IEP" url = f "https://files. rcsb. org/download/ { pdb_id }. pdb" response = requests. get ( url ) pdb_content = response. text Let's see what's inside the pdb file: # Parse the PDB content to identify chains and ligands chains = {} for line in pdb_content. splitlines (): if line. startswith ( "ATOM" ) or line. startswith ( "HETATM" ): chain_id = line [ 21 ] resn = line [ 17 : 20 ]. strip () if chain_id not in chains : chains [ chain_id ] = { 'residues' : set (), 'ligands' : set ()} chains [ chain_id ][ 'residues' ]. add ( resn ) if line. startswith ( "HETATM" ): chains [ chain_id ][ 'ligands' ]. add ( resn ) chains_info = chains print ( "Chains and Ligands Information:" ) for chain_id, info in chains_info. items (): print ( f "Chain { chain_id } :" ) print ( f " Residues: { sorted ( info [ 'residues' ]) } " ) print ( f " Ligands: { sorted ( info [ 'ligands' ]) } " ) Chains and Ligands Information: Chain A: Residues: ['ALA', 'ARG', 'ASN', 'ASP', 'CL', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'HOH', 'ILE', 'LEU', 'LYS', 'M ET', 'PHE', 'PRO', 'SER', 'STI', 'THR', 'TRP', 'TYR', 'VAL'] Ligands: ['CL', 'HOH', 'STI'] Chain B: Residues: ['ALA', 'ARG', 'ASN', 'ASP', 'CL', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'HOH', 'ILE', 'LEU', 'LYS', 'M ET', 'PHE', 'PRO', 'SER', 'STI', 'THR', 'TRP', 'TYR', 'VAL'] Ligands: ['CL', 'HOH', 'STI'] We have a homodimer of two identical chains and several ligands: CL, HOH, and STI. CL and HOH are common solvents and ions binding to the protein structure, while STI is the ligand of interest (Imatinib). For visualization, let's extract the information of chain A and its ligands: chain_id = "A" chain_lines = [] for line in pdb_content. splitlines (): if line. startswith ( "HETATM" ) or line. startswith ( "ATOM" ): if line [ 21 ] == chain_id : chain_lines. append ( line ) elif line. startswith ( "TER" ): if chain_lines and chain_lines [-1 ][ 21 ] == chain_id : chain_lines. append ( line ) chain_A = " \n ". join ( chain_lines ) view = py3Dmol. view () # Add the extracted chain model view. add Model ( chain_A, "pdb" ) # Set the styles view. set Style ({ 'cartoon' : { 'color' : 'darkgreen' }}) for ligand in chains_info [ chain_id ][ 'ligands' ]:

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view. add Style ({ 'chain' : 'A', 'resn' : ligand }, { 'stick' : { 'colorscheme' : 'yellow Carbon' }}) view. zoom To () view. show () Now let's highlight the binding pocket in the protein ribbon. For this purpose we need to parse the PDB content again to identify residues belonging to chain A and STI ligand: parser = PDBParser () structure = parser. get_structure ( 'protein', String IO ( pdb_content )) # Extract residues of chain A and the ligand STI str_chain_A = structure [ 0 ][ chain_id ] ligand = None ligand_resname = "STI" for residue in structure. get_residues (): if residue. id [ 0 ] != ' ' and residue. resname == ligand_resname : ligand = residue break if ligand is None : raise Value Error ( "Ligand STi not found in the PDB content" ) chain_A_atoms = [ atom for atom in str_chain_A. get_atoms ()] ligand_atoms = [ atom for atom in ligand. get_atoms ()] /usr/local/lib/python3. 10/dist-packages/Bio/PDB/Structure Builder. py:89: PDBConstruction Warning: WARNING: Chain A is discontinuous at line 5038. warnings. warn( /usr/local/lib/python3. 10/dist-packages/Bio/PDB/Structure Builder. py:89: PDBConstruction Warning: WARNING: Chain B is discontinuous at line 5079. warnings. warn( /usr/local/lib/python3. 10/dist-packages/Bio/PDB/Structure Builder. py:89: PDBConstruction Warning: WARNING: Chain A is discontinuous at line 5118. warnings. warn( /usr/local/lib/python3. 10/dist-packages/Bio/PDB/Structure Builder. py:89: PDBConstruction Warning: WARNING: Chain B is discontinuous at line 5217. warnings. warn( Now we can find the residues in chain A within a certain distance from the ligand. Here we set this distance to 5Å. binding_residues = set () distance_threshold = 5. 0 ns = Neighbor Search ( chain_A_atoms ) for ligand_atom in ligand_atoms : close_atoms = ns. search ( ligand_atom. coord, distance_threshold ) for atom in close_atoms : binding_residues. add ( atom. get_parent (). id [ 1 ]) And let's see the visualization, now with the binding pocket: # Create a py3Dmol view view = py3Dmol. view ()

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# Add the chain model view. add Model ( chain_A, "pdb" ) # Set the style for the protein view. set Style ({ 'cartoon' : { 'color' : 'darkgreen' }}) # Color the binding pocket residues for resi in binding_residues : view. add Style ({ 'chain' : chain_id, 'resi' : str ( resi )}, { 'cartoon' : { 'colorscheme' : 'green Carbon' }}) # Color the ligand view. add Style ({ 'chain' : chain_id, 'resn' : ligand_resname }, { 'stick' : { 'colorscheme' : 'yellow Carbon' }}) # Zoom to the view view. zoom To () # Show the view view. show () Further Reading For further reading on computational methods for binding sites and protein language models here are a couple of great resources: Exploring the computational methods for protein-ligand binding site prediction -Getting started with protein language models Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!

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Citing this tutorial If you found this tutorial useful please consider citing it using the provided Bib Te X. @manual{Bioinformatics, title={Introduction to Binding Sites}, organization={Deep Chem}, author={Gómez de Lope, Elisa}, howpublished = {\url{https://github. com/deepchem/deepchem/blob/master/examples/tutorials/Introduction_to_Binding_Sites. ipynb}}, year={2024}, }

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Tutorial Part 13: Modeling Protein-Ligand Interactions By Nathan C. Frey | Twitter and Bharath Ramsundar | Twitter In this tutorial, we'll walk you through the use of machine learning and molecular docking methods to predict the binding energy of a protein-ligand complex. Recall that a ligand is some small molecule which interacts (usually non-covalently) with a protein. Molecular docking performs geometric calculations to find a “binding pose” with a small molecule interacting with a protein in a suitable binding pocket (that is, a region on the protein which has a groove in which the small molecule can rest). The structure of proteins can be determined experimentally with techniques like Cryo-EM or X-ray crystallography. This can be a powerful tool for structure-based drug discovery. For more info on docking, read the Auto Dock Vina paper and the deepchem. dock documentation. There are many graphical user and command line interfaces (like Auto Dock) for performing molecular docking. Here, we show how docking can be performed programmatically with Deep Chem, which enables automation and easy integration with machine learning pipelines. As you work through the tutorial, you'll trace an arc including 1. Loading a protein-ligand complex dataset ( PDBbind ) 2. Performing programmatic molecular docking 3. Featurizing protein-ligand complexes with interaction fingerprints 4. Fitting a random forest model and predicting binding affinities To start the tutorial, we'll use a simple pre-processed dataset file that comes in the form of a gzipped file. Each row is a molecular system, and each column represents a different piece of information about that system. For instance, in this example, every row reflects a protein-ligand complex, and the following columns are present: a unique complex identifier; the SMILES string of the ligand; the binding affinity (Ki) of the ligand to the protein in the complex; a Python list of all lines in a PDB file for the protein alone; and a Python list of all lines in a ligand file for the ligand alone. Colab This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup To run Deep Chem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. ! pip install -q condacolab import condacolab condacolab. install () WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the syst em package manager. It is recommended to use a virtual environment instead: https://pip. pypa. io/warnings/venv ✨✨ Everything looks OK! ! conda install -c conda-forge openmm Collecting package metadata (current_repodata. json): - ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​ done Solving environment: \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​ done # All requested packages already installed. ! pip install deepchem

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Looking in indexes: https://pypi. org/simple, https://us-python. pkg. dev/colab-wheels/public/simple/ Requirement already satisfied: deepchem in /usr/local/lib/python3. 10/site-packages (2. 7. 1) Requirement already satisfied: numpy>=1. 21 in /usr/local/lib/python3. 10/site-packages (from deepchem) (1. 24. 3) Requirement already satisfied: scikit-learn in /usr/local/lib/python3. 10/site-packages (from deepchem) (1. 2. 2) Requirement already satisfied: joblib in /usr/local/lib/python3. 10/site-packages (from deepchem) (1. 2. 0) Requirement already satisfied: scipy<1. 9 in /usr/local/lib/python3. 10/site-packages (from deepchem) (1. 8. 1) Requirement already satisfied: pandas in /usr/local/lib/python3. 10/site-packages (from deepchem) (2. 0. 1) Requirement already satisfied: rdkit in /usr/local/lib/python3. 10/site-packages (from deepchem) (2023. 3. 1) Requirement already satisfied: python-dateutil>=2. 8. 2 in /usr/local/lib/python3. 10/site-packages (from pandas->d eepchem) (2. 8. 2) Requirement already satisfied: pytz>=2020. 1 in /usr/local/lib/python3. 10/site-packages (from pandas->deepchem) ( 2023. 3) Requirement already satisfied: tzdata>=2022. 1 in /usr/local/lib/python3. 10/site-packages (from pandas->deepchem) (2023. 3) Requirement already satisfied: Pillow in /usr/local/lib/python3. 10/site-packages (from rdkit->deepchem) (9. 5. 0) Requirement already satisfied: threadpoolctl>=2. 0. 0 in /usr/local/lib/python3. 10/site-packages (from scikit-lear n->deepchem) (3. 1. 0) Requirement already satisfied: six>=1. 5 in /usr/local/lib/python3. 10/site-packages (from python-dateutil>=2. 8. 2->pandas->deepchem) (1. 16. 0) WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the syst em package manager. It is recommended to use a virtual environment instead: https://pip. pypa. io/warnings/venv ! conda install -c conda-forge pdbfixer Collecting package metadata (current_repodata. json): - ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ done Solving environment: | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ done # All requested packages already installed. ! conda install -c conda-forge vina Collecting package metadata (current_repodata. json): - ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ done Solving environment: / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​- ​​ \ ​​ | ​​ / ​​ done # All requested packages already installed. Protein-ligand complex data It is really helpful to visualize proteins and ligands when doing docking. Unfortunately, Google Colab doesn't currently support the Jupyter widgets we need to do that visualization. Install MDTraj and nglview on your local machine to view the protein-ligand complexes we're working with. ! pip install -q mdtraj nglview # !jupyter-nbextension enable nglview --py --sys-prefix # for jupyter notebook # !jupyter labextension install nglview-js-widgets # for jupyter lab WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the syst em package manager. It is recommended to use a virtual environment instead: https://pip. pypa. io/warnings/venv import os import numpy as np import pandas as pd import tempfile from rdkit import Chem from rdkit. Chem import All Chem import deepchem as dc from deepchem. utils import download_url, load_from_disk Skipped loading modules with pytorch-geometric dependency, missing a dependency. No module named 'torch_geometri c' Skipped loading modules with pytorch-geometric dependency, missing a dependency. cannot import name 'DMPNN' from 'deepchem. models. torch_models' (/usr/local/lib/python3. 10/site-packages/deepchem/models/torch_models/__init__. py ) Skipped loading modules with pytorch-lightning dependency, missing a dependency. No module named 'pytorch_lightn ing' Skipped loading some Jax models, missing a dependency. No module named 'haiku' To illustrate the docking procedure, here we'll use a csv that contains SMILES strings of ligands as well as PDB files for the ligand and protein targets from PDBbind. Later, we'll use the labels to train a model to predict binding affinities. We'll also show how to download and featurize PDBbind to train a model from scratch.

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data_dir = dc. utils. get_data_dir () dataset_file = os. path. join ( data_dir, "pdbbind_core_df. csv. gz" ) if not os. path. exists ( dataset_file ): print ( 'File does not exist. Downloading file... ' ) download_url ( "https://s3-us-west-1. amazonaws. com/deepchem. io/datasets/pdbbind_core_df. csv. gz" ) print ( 'File downloaded... ' ) raw_dataset = load_from_disk ( dataset_file ) raw_dataset = raw_dataset [[ 'pdb_id', 'smiles', 'label' ]] Let's see what raw_dataset looks like: raw_dataset. head ( 2 ) pdb_id smiles label 0 2d3u CC1CCCCC1S(O)(O)NC1CC(C2CCC(CN)CC2)SC1C(O)O 6. 92 1 3cyx CC(C)(C)NC(O)C1CC2CCCCC2C[NH+]1CC(O)C(CC1CCCCC... 8. 00 Fixing PDB files Next, let's get some PDB protein files for visualization and docking. We'll use the PDB IDs from our raw_dataset and download the pdb files directly from the Protein Data Bank using pdbfixer. We'll also sanitize the structures with RDKit. This ensures that any problems with the protein and ligand files (non-standard residues, chemical validity, etc. ) are corrected. Feel free to modify these cells and pdbids to consider new protein-ligand complexes. We note here that PDB files are complex and human judgement is required to prepare protein structures for docking. Deep Chem includes a number of docking utilites to assist you with preparing protein files, but results should be inspected before docking is attempted. from openmm. app import PDBFile from pdbfixer import PDBFixer from deepchem. utils. vina_utils import prepare_inputs # consider one protein-ligand complex for visualization pdbid = raw_dataset [ 'pdb_id' ]. iloc [ 1 ] ligand = raw_dataset [ 'smiles' ]. iloc [ 1 ] %%time fixer = PDBFixer ( pdbid = pdbid ) PDBFile. write File ( fixer. topology, fixer. positions, open ( ' %s. pdb' % ( pdbid ), 'w' )) p, m = None, None # fix protein, optimize ligand geometry, and sanitize molecules try : p, m = prepare_inputs ( ' %s. pdb' % ( pdbid ), ligand ) except : print ( ' %s failed PDB fixing' % ( pdbid )) if p and m : # protein and molecule are readable by RDKit print ( pdbid, p. Get Num Atoms ()) Chem. rdmolfiles. Mol To PDBFile ( p, ' %s. pdb' % ( pdbid )) Chem. rdmolfiles. Mol To PDBFile ( m, 'ligand_ %s. pdb' % ( pdbid )) <timed exec>:7: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. Warning: importing 'simtk. openmm' is deprecated. Import 'openmm' instead. 3cyx 1510 CPU times: user 2. 04 s, sys: 157 ms, total: 2. 2 s Wall time: 4. 32 s Visualization If you're outside of Colab, you can expand these cells and use MDTraj and nglview to visualize proteins and ligands. import mdtraj as md import nglview from IPython. display import display, Image Let's take a look at the first protein ligand pair in our dataset: protein_mdtraj = md. load_pdb ( '3cyx. pdb' ) ligand_mdtraj = md. load_pdb ( 'ligand_3cyx. pdb' )

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We'll use the convenience function nglview. show_mdtraj in order to view our proteins and ligands. Note that this will only work if you uncommented the above cell, installed nglview, and enabled the necessary notebook extensions. v = nglview. show_mdtraj ( ligand_mdtraj ) display ( v ) # interactive view outside Colab NGLWidget() Now that we have an idea of what the ligand looks like, let's take a look at our protein: view = nglview. show_mdtraj ( protein_mdtraj ) display ( view ) # interactive view outside Colab NGLWidget() Molecular Docking Ok, now that we've got our data and basic visualization tools up and running, let's see if we can use molecular docking to estimate the binding affinities between our protein ligand systems. There are three steps to setting up a docking job, and you should experiment with different settings. The three things we need to specify are 1) how to identify binding pockets in the target protein; 2) how to generate poses (geometric configurations) of a ligand in a binding pocket; and 3) how to "score" a pose. Remember, our goal is to identify candidate ligands that strongly interact with a target protein, which is reflected by the score. Deep Chem has a simple built-in method for identifying binding pockets in proteins. It is based on the convex hull method. The method works by creating a 3D polyhedron (convex hull) around a protein structure and identifying the surface atoms of the protein as the ones closest to the convex hull. Some biochemical properties are considered, so the method is not purely geometrical. It has the advantage of having a low computational cost and is good enough for our purposes. finder = dc. dock. binding_pocket. Convex Hull Pocket Finder () pockets = finder. find_pockets ( '3cyx. pdb' ) len ( pockets ) # number of identified pockets 36 Pose generation is quite complex. Luckily, using Deep Chem's pose generator will install the Auto Dock Vina engine under

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the hood, allowing us to get up and running generating poses quickly. vpg = dc. dock. pose_generation. Vina Pose Generator () We could specify a pose scoring function from deepchem. dock. pose_scoring, which includes things like repulsive and hydrophobic interactions and hydrogen bonding. Vina will take care of this, so instead we'll allow Vina to compute scores for poses. ! mkdir -p vina_test %%time complexes, scores = vpg. generate_poses ( molecular_complex = ( '3cyx. pdb', 'ligand_3cyx. pdb' ), # protein-ligand files for docking, out_dir = 'vina_test', generate_scores = True ) CPU times: user 41min 4s, sys: 21. 9 s, total: 41min 26s Wall time: 28min 32s /usr/local/lib/python3. 10/site-packages/vina/vina. py:260: Deprecation Warning: `np. int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np. int`, you may wish to use e. g. `np. int64` or `np. int32` to specify the precision. If yo u wish to review your current use, check the release note link for additional information. Deprecated in Num Py 1. 20; for more details and guidance: https://numpy. org/devdocs/release/1. 20. 0-notes. html#dep recations self. _voxels = np. ceil(np. array(box_size) / self. _spacing). astype(np. int) We used the default value for num_modes when generating poses, so Vina will return the 9 lowest energy poses it found in units of kcal/mol. scores [-9. 484, -9. 405, -9. 195, -9. 151, -8. 9, -8. 696, -8. 687, -8. 633, -8. 557] Can we view the complex with both protein and ligand? Yes, but we'll need to combine the molecules into a single RDkit molecule. complex_mol = Chem. Combine Mols ( complexes [ 0 ][ 0 ], complexes [ 0 ][ 1 ]) Let's now visualize our complex. We can see that the ligand slots into a pocket of the protein. v = nglview. show_rdkit ( complex_mol ) display ( v ) NGLWidget() Now that we understand each piece of the process, we can put it all together using Deep Chem's Docker class. Docker creates a generator that yields tuples of posed complexes and docking scores. docker = dc. dock. docking. Docker ( pose_generator = vpg ) posed_complex, score = next ( docker. dock ( molecular_complex = ( '3cyx. pdb', 'ligand_3cyx. pdb' ), use_pose_generator_scores = True )) Modeling Binding Affinity Docking is a useful, albeit coarse-grained tool for predicting protein-ligand binding affinities. However, it takes some time, especially for large-scale virtual screenings where we might be considering different protein targets and thousands of potential ligands. We might naturally ask then, can we train a machine learning model to predict docking scores? Let's try and find out!

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We'll show how to download the PDBbind dataset. We can use the loader in Molecule Net to get the 4852 protein-ligand complexes from the "refined" set or the entire "general" set in PDBbind. For simplicity, we'll stick with the ~100 complexes we've already processed to train our models. Next, we'll need a way to transform our protein-ligand complexes into representations which can be used by learning algorithms. Ideally, we'd have neural protein-ligand complex fingerprints, but Deep Chem doesn't yet have a good learned fingerprint of this sort. We do however have well-tuned manual featurizers that can help us with our challenge here. We'll make use of two types of fingerprints in the rest of the tutorial, the Circular Fingerprint and Contact Circular Fingerprint. Deep Chem also has voxelizers and grid descriptors that convert a 3D volume containing an arragment of atoms into a fingerprint. These featurizers are really useful for understanding protein-ligand complexes since they allow us to translate complexes into vectors that can be passed into a simple machine learning algorithm. First, we'll create circular fingerprints. These convert small molecules into a vector of fragments. pdbids = raw_dataset [ 'pdb_id' ]. values ligand_smiles = raw_dataset [ 'smiles' ]. values %%time for ( pdbid, ligand ) in zip ( pdbids, ligand_smiles ): fixer = PDBFixer ( url = 'https://files. rcsb. org/download/ %s. pdb' % ( pdbid )) PDBFile. write File ( fixer. topology, fixer. positions, open ( ' %s. pdb' % ( pdbid ), 'w' )) p, m = None, None # skip pdb fixing for speed try : p, m = prepare_inputs ( ' %s. pdb' % ( pdbid ), ligand, replace_nonstandard_residues = False, remove_heterogens = False, remove_water = False, add_hydrogens = False ) except : print ( ' %s failed sanitization' % ( pdbid )) if p and m : # protein and molecule are readable by RDKit Chem. rdmolfiles. Mol To PDBFile ( p, ' %s. pdb' % ( pdbid )) Chem. rdmolfiles. Mol To PDBFile ( m, 'ligand_ %s. pdb' % ( pdbid )) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:11:45] UFFTYPER: Unrecognized atom type: S_5+4 (7) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. 3cyx failed sanitization <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:02] UFFTYPER: Warning: hybridization set to SP3 for atom 17 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:04] UFFTYPER: Warning: hybridization set to SP3 for atom 6 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:06] UFFTYPER: Warning: hybridization set to SP3 for atom 1 [15:12:06] UFFTYPER: Unrecognized atom type: S_5+4 (21) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:23] UFFTYPER: Warning: hybridization set to SP3 for atom 20 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils.

deepchem.pdf

<timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:31] UFFTYPER: Warning: hybridization set to SP3 for atom 19 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:35] UFFTYPER: Warning: hybridization set to SP3 for atom 29 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:13:03] UFFTYPER: Unrecognized atom type: S_5+4 (39) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:13:37] UFFTYPER: Warning: hybridization set to SP3 for atom 33 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:01] UFFTYPER: Unrecognized atom type: S_5+4 (11) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:02] UFFTYPER: Unrecognized atom type: S_5+4 (47) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:14] UFFTYPER: Unrecognized atom type: S_5+4 (1)

deepchem.pdf

<timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:27] UFFTYPER: Warning: hybridization set to SP3 for atom 6 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:33] UFFTYPER: Unrecognized atom type: S_5+4 (47) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:43] UFFTYPER: Unrecognized atom type: S_5+4 (28) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:55] UFFTYPER: Warning: hybridization set to SP3 for atom 17 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:14:57] UFFTYPER: Warning: hybridization set to SP3 for atom 6 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:08] Explicit valence for atom # 388 O, 3, is greater than permitted <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:15] UFFTYPER: Warning: hybridization set to SP3 for atom 9 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:19] UFFTYPER: Unrecognized atom type: S_5+4 (6) 3utu failed sanitization <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:29] UFFTYPER: Unrecognized atom type: S_5+4 (1) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun

deepchem.pdf

ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:39] UFFTYPER: Unrecognized atom type: S_5+4 (19) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:43] UFFTYPER: Unrecognized atom type: S_5+4 (21) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:15:57] UFFTYPER: Unrecognized atom type: S_5+4 (9) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:16:01] UFFTYPER: Warning: hybridization set to SP3 for atom 18 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:16:21] UFFTYPER: Warning: hybridization set to SP3 for atom 17 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:16:42] UFFTYPER: Warning: hybridization set to SP3 for atom 10 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:17:19] UFFTYPER: Unrecognized atom type: S_5+4 (13) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun

deepchem.pdf

ction in deepchem. utils. docking_utils. [15:17:25] UFFTYPER: Unrecognized atom type: S_5+4 (10) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:17:27] UFFTYPER: Unrecognized atom type: S_5+4 (6) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:17:28] UFFTYPER: Warning: hybridization set to SP3 for atom 11 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:17:46] UFFTYPER: Unrecognized atom type: S_5+4 (8) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:17:58] UFFTYPER: Unrecognized atom type: S_5+4 (4) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:18:02] UFFTYPER: Unrecognized atom type: S_5+4 (9) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:18:15] UFFTYPER: Unrecognized atom type: S_5+4 (1) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:18:32] UFFTYPER: Unrecognized atom type: S_5+4 (23) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:18:35] UFFTYPER: Unrecognized atom type: S_5+4 (22) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:18:42] UFFTYPER: Warning: hybridization set to SP3 for atom 8 [15:18:42] UFFTYPER: Warning: hybridization set to SP3 for atom 24 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun

deepchem.pdf

ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:01] UFFTYPER: Warning: hybridization set to SP3 for atom 16 [15:19:01] UFFTYPER: Unrecognized atom type: S_5+4 (20) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:02] UFFTYPER: Unrecognized atom type: S_5+4 (6) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:05] UFFTYPER: Unrecognized atom type: S_5+4 (6) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. 1hfs failed sanitization <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:22] UFFTYPER: Warning: hybridization set to SP3 for atom 20 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:41] Explicit valence for atom # 1800 C, 5, is greater than permitted [15:19:41] UFFTYPER: Unrecognized atom type: S_5+4 (11) <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:42] UFFTYPER: Warning: hybridization set to SP3 for atom 11 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:57] UFFTYPER: Warning: hybridization set to SP3 for atom 9 [15:19:57] UFFTYPER: Warning: hybridization set to SP3 for atom 23 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:19:59] UFFTYPER: Warning: hybridization set to SP3 for atom 8 [15:19:59] UFFTYPER: Warning: hybridization set to SP3 for atom 12 [15:19:59] UFFTYPER: Warning: hybridization set to SP3 for atom 34 [15:19:59] UFFTYPER: Warning: hybridization set to SP3 for atom 41 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. CPU times: user 4min 9s, sys: 3. 31 s, total: 4min 12s Wall time: 8min 19s proteins = [ f for f in os. listdir ( '. ' ) if len ( f ) == 8 and f. endswith ( '. pdb' )] ligands = [ f for f in os. listdir ( '. ' ) if f. startswith ( 'ligand' ) and f. endswith ( '. pdb' )] We'll do some clean up to make sure we have a valid ligand file for every valid protein. The lines here will compare the PDB IDs between the ligand and protein files and remove any proteins that don't have corresponding ligands. # Handle failed sanitizations failures = set ([ f [:-4 ] for f in proteins ]) - set ([ f [ 7 :-4 ] for f in ligands ]) for pdbid in failures : proteins. remove ( pdbid + '. pdb' )

deepchem.pdf

len ( proteins ), len ( ligands ) (190, 190) pdbids = [ f [:-4 ] for f in proteins ] small_dataset = raw_dataset [ raw_dataset [ 'pdb_id' ]. isin ( pdbids )] labels = small_dataset. label fp_featurizer = dc. feat. Circular Fingerprint ( size = 2048 ) features = fp_featurizer. featurize ([ Chem. Mol From PDBFile ( l ) for l in ligands ]) dataset = dc. data. Numpy Dataset ( X = features, y = labels, ids = pdbids ) train_dataset, test_dataset = dc. splits. Random Splitter (). train_test_split ( dataset, seed = 42 ) The convenience loader dc. molnet. load_pdbbind will take care of downloading and featurizing the pdbbind dataset under the hood for us. This will take quite a bit of time and compute, so the code to do it is commented out. Uncomment it and grab a cup of coffee if you'd like to featurize all of PDBbind's refined set. Otherwise, you can continue with the small dataset we constructed above. # # Uncomment to featurize all of PDBBind's "refined" set # pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc. molnet. load_pdbbind( # featurizer=fp_featurizer, set_name="refined", reload=True, # data_dir='pdbbind_data', save_dir='pdbbind_data') Now, we're ready to do some learning! To fit a deepchem model, first we instantiate one of the provided (or user-written) model classes. In this case, we have a created a convenience class to wrap around any ML model available in Sci-Kit Learn that can in turn be used to interoperate with deepchem. To instantiate an Sklearn Model, you will need (a) task_types, (b) model_params, another dict as illustrated below, and (c) a model_instance defining the type of model you would like to fit, in this case a Random Forest Regressor. from sklearn. ensemble import Random Forest Regressor from deepchem. utils. evaluate import Evaluator import pandas as pd seed = 42 # Set a random seed to get stable results sklearn_model = Random Forest Regressor ( n_estimators = 100, max_features = 'sqrt' ) sklearn_model. random_state = seed model = dc. models. Sklearn Model ( sklearn_model ) model. fit ( train_dataset ) Note that the value for the test set indicates that the model isn't producing meaningful outputs. It turns out that predicting binding affinities is hard. This tutorial isn't meant to show how to create a state-of-the-art model for predicting binding affinities, but it gives you the tools to generate your own datasets with molecular docking, featurize complexes, and train models. # use Pearson correlation so metrics are > 0 metric = dc. metrics. Metric ( dc. metrics. pearson_r2_score ) evaluator = Evaluator ( model, train_dataset, []) train_r2score = evaluator. compute_model_performance ([ metric ]) print ( "RF Train set R^2 %f " % ( train_r2score [ "pearson_r2_score" ])) evaluator = Evaluator ( model, test_dataset, []) test_r2score = evaluator. compute_model_performance ([ metric ]) print ( "RF Test set R^2 %f " % ( test_r2score [ "pearson_r2_score" ])) RF Train set R^2 0. 888697 RF Test set R^2 0. 007797 We're using a very small dataset and an overly simplistic representation, so it's no surprise that the test set performance is quite bad. # Compare predicted and true values list ( zip ( model. predict ( train_dataset ), train_dataset. y ))[: 5 ] [(6. 862549999999994, 7. 4), (6. 616400000000008, 6. 85), (4. 852004999999995, 3. 4), (6. 43060000000001, 6. 72), (8. 66322999999999, 11. 06)] list ( zip ( model. predict ( test_dataset ), test_dataset. y ))[: 5 ]

deepchem.pdf

[(5. 960549999999999, 4. 21), (6. 051305714285715, 8. 7), (5. 799900000000003, 6. 39), (6. 433881666666665, 4. 94), (6. 7465399999999995, 9. 21)] The protein-ligand complex view. In the previous section, we featurized only the ligand. This time, let's see if we can do something sensible with our protein-ligand fingerprints that make use of our structural information. To start with, we need to re-featurize the dataset but using the contact fingerprint this time. fp_featurizer = dc. feat. Contact Circular Fingerprint ( size = 2048 ) features = fp_featurizer. featurize ( zip ( ligands, proteins )) dataset = dc. data. Numpy Dataset ( X = features, y = labels, ids = pdbids ) train_dataset, test_dataset = dc. splits. Random Splitter (). train_test_split ( dataset, seed = 42 ) [15:21:40] Explicit valence for atom # 3 C, 5, is greater than permitted Mol [H]OC([H])([H])[C@]1([H])O[C@@]2([H])SC([H])([H])(N([H])C([H])([H])C([H])([H])[H])N([H])[C@@]2([H])[C@@]([H] )(O[H])[C@]1([H])O[H] failed sanitization [15:21:47] Explicit valence for atom # 214 O, 3, is greater than permitted Mol CC[C@H](C)[C@@H]1NC(=O)CNC(=O)[C@H](CCSC)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](CC2=CN C3=C2C=CC=C3)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC2=CC=CC=C2)NC(=O)[C@@H ]2CCCN2C(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC)NC(=O)[C@H](C(C)C)NC(=O)[C@H](CC2=CC=CC=C2)NC(=O)[C@@H]2 CCCN2C(=O)CNC(=O)CNC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](C)NC(= O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCCN)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(=O)O) NC(=O)[C@@H]2CCCN2C(=O)[C@H](CCCCN)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)CNC(=O)[C@H](C)NC(=O)[C@@H]2CSSC[C@H](NC(=O )[C@@H](NC(=O)[C@@H]3CCCN3C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H]3CCCN 3C(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[ C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H]3CCCN3C(=O)[C@H](CCC(N)=O)NC(=O)CN)C(C)C)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)C( =O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@ H]([C@@H](C)CC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)O)C(= O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC3=CC=CC=C3)C(=O)N2)CSSC[C@@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@ H](CCCNC(=N)N)C(=O)N[C@@H](CC(=O)O)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)NCC(=O)N[C@@H](CC 2=CC=CC=C2)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@H](C(=O)N[C@@H](CC2=CNC=N2)C(=O)N[C@H](C(=O)N[C@@H](CC2=CC=CC=C 2)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC2=CNC3=C2C=CC =C3)C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@ H](CCC(N)=O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N2C3C(=O)O32)[C@@H](C)CC)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)O)NC(=O)CNC (=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CC2=CNC3=C2C=CC=C3)NC(=O)[C@H](CO)NC(=O)[C@H](C(C)C)NC1=O. CC[C@H](C)[C@H]( NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@]12C(CC(=O)O)C13C(C)[C@H]( N)C(=O)N32)C(=O)NCC(=O)N[C@@H](CCSC)C(=O)N[C@@H](CO)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H] (CCC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCCNC(=N)N)C( =O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CC(C)C)C(=O)N[C@H]1CSSC[C@@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC2=CC=C(O)C=C2 )C(=O)N2CCC[C@H]2C(=O)N2CCC[C@H]2C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[ C@@H](CC(N)=O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(= O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)NCC(=O)N[C@@H]( CCCCN)C(=O)N[C@@H](CC2=CNC=N2)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)N[C@H]2CCCN(C(=N)N)C(C(=O )O)C[C@@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCCN)C(= O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C @H](C(=O)N[C@@H](CC3=CC=C(O)C=C3)C(=O)N[C@H](C(=O)N[C@@H](CC3=CNC=N3)C(=O)N3CCC[C@H]3C(=O)N[C@@H](CCCNC(=N)N)C(= O)N[C@@H](CC3=CC=C(O)C=C3)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC3=CNC4=C3C=CC=C4)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@ @H](CCC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H]( CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C) C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N3CCC[C@H]3C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC3=CC=CC=C3)C(=O) N[C@@H](CO)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC3=CC=C(O)C=C3)C(=O)N[C@H](C(=O)N[C@@H](CC3=CNC=N3)C(=O)N3CCC[C@H] 3C(=O)N[C@H](C(=O)N[C@@H](CSSC[C@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)N)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]( CCCNC(=N)N)C(=O)N3CCC[C@H]3C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC3=CC=CC=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCC N)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H] (CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O) N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@]34C(=O)O3C4C3=CC=C(O)C=C3)[C@@H](C)O)C(=O)N[C@@H ](CC(C)C)C(=O)N3CCC[C@H]3C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C @@H](C)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H] (C)C(=O)NCC(=O)N[C@@H](CC3=CC=C(O)C=C3)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)N[C@H] (C(=O)NCC(=O)N[C@@H](CC3=CNC4=C3C=CC=C4)C(=O)NCC(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O) N[C@H](C=O)CCC(=O)O)[C@@H](C)O)C(C)C)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)CC)[C@@H](C)CC)[C@@ H](C)CC)[C@@H](C)CC)NC(=O)[C@H](CC3=CC=C(O)C=C3)NC2=O)[C@@H](C)O)[C@@H](C)CC)C(C)C)[C@@H](C)O)NC(=O)[C@H](CC2=CN C=N2)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C(C)C)NC(=O)[C@H](CC2=CNC 3=C2C=CC=C3)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H]([C@@H](C)CC)NC(=O)[C@H](CC(C)C )NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)CNC1=O)C(C)C. CC[C@H](C)[C@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O )[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](N)CC(=O)O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@ H](CO)CC1CCC(OS(O)(O)O)CC1. CO[C@H]1CC[C@H](S(O)(O)N[C@@H](C[C@@H](O)NC[C@H]2CC[C@H](CN)CC2)C(O)N2CCC[C@H]2[C@H]( O)NCC2CCC(C(N)N)CC2)CC1Cl. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. [Na H]. [Na H] failed sanitization [15:21:58] Explicit valence for atom # 9 C, 5, is greater than permitted Mol [H]O[C@@]1([H])[C@@]([H])(O[H])[C@@]2([H])N([C@@]([H])(O[H])[C@]1([H])O[H])C([H])([H])(N([H])C([H])([H])C([H ])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])OC2([H])[H] failed sanitization

deepchem.pdf

[15:22:27] Explicit valence for atom # 5358 O, 3, is greater than permitted Mol CC(O)O. CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CCCCN)NC(=O)CNC(=O)[C@@H]1CC(O)=OO=C([C@H](CCCCN)NC(= O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC2=CC=CC=C2)NC(=O)[C@@H](NC(=O)[C @H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@H](C)N C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@ @H](NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC2=CC=CC=C2)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CC2=CC= C(O)C=C2)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCSC)NC(=O)CNC(=O)[C@@H]2CCCN2C(=O) [C@H](C)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC 2=CNC=N2)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC2=CNC3=C2C=CC=C3 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deepchem.pdf

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O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O.

deepchem.pdf

O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. 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C(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C 1C=CC=C2)NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H]1CCCN1C(=O)[ C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H]( NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H ]1CCCN1C(=O)[C@H](CCC(N)=O)NC(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](C )NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@ @H]1CCCN1C(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCCN)NC(=O)[C@@H ](NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=C C=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCNC(=N )N)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1 )NC(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H]( CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC1=CC= C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]( NC(=O)CNC(=O)[C@H](CCSC)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O) [C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC= N1)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC (=O)CNC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCNC(=N) N)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)CNC(=O )[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCCCN)NC( =O)[C@H](CC(N)=O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H ](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@ H](CCC(N)=O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@ H](CC1=CC=C(O)C=C1)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)N C(=O)[C@H](CCCCN)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC1=CC=C(O)C=C1)

deepchem.pdf

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)C)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H] (NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O) [C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O) [C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@ H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(N)=O)NC(=O)CNC(=O)[C@H] (CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=

deepchem.pdf

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Cl. Cl. Cl. Cl. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O failed sanitization [15:23:10] Explicit valence for atom # 2174 O, 3, is greater than permitted Mol CC(C)[C@H](NC(=O)[C@H](CO)NC(=O)[C@H](C)N)C(=O)N[C@@H](CO)C(=O)N1O2C(=O)[C@]12C. CC[C@H](C)[C@H](NC(=O)CNC(=O )[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](C) NC(=O)[C@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[ C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]1CCC N1C(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCSC)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]1CCC N1C(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@ H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)CNC(=O)[C@H](CO)NC(=O)[C@H](CS)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@ H](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@ H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCN C(=N)N)NC(=O)[C@H](CC1=CNC=N1)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCCN)NC(= O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(N)=O)NC(=O) [C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CS)NC(=O)[C@@H](NC( =O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@@H](N)CC(N)=O)[C@@H](C)CC)[C@@H](C)C C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)CC)[C@@H](C)CC)C(C)C)C(C)C)C(=O)NCC(=O)N[C@H](C(=O)N[C@H](C(=O)N[ C@@H](CC1=CNC=N1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N1CCC[C@H]1C(= O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H] (CC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC (C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)NCC(=O)N

deepchem.pdf

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CC[C@H](C)[C@H](NC(=O)[C@H](CCCCN)NC(=O)[C@@H]( NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC( =O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O )[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O) CNC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O) [C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C @@H]1CCCN1C(=O)[C@@H](N)C(C)C)C(C)C)C(C)C)[C@@H](C)O)C(C)C)C(C)C)C(C)C)[C@@H](C)O)C(C)C)C(C)C)C(=O)N[C@H](C(=O)N [C@@H](CC(=O)O)C(=O)N[C@@H](CCSC)C(=O)N[C@H](C=O)CCCCN)C(C)C. CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)N1C3CCCCC3C3C4C (C(O)N[C@@H]4O)C4C5CCCCC5N2C4C31. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. 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OS(O)(O)O failed sanitization [15:23:15] Explicit valence for atom # 1823 O, 4, is greater than permitted Mol CC[C@H](C)[C@H](N)C(=O)N[C@H](C(=O)NCC(=O)NCC(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H]1CSSC[C@@ H](C(=O)N2CCC[C@H]2C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O )N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(N)= O)C(=O)N[C@@H](CCC(=O)O)C(=O)NCC(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@H]2CSSC[C@@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H] (CC3=CC=C(O)C=C3)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC 3=CC=CC=C3)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N [C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC( 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C(=O)[C@@H](NC(=O)[C@@H]3CCCN3C(=O)[C@@H](NC(=O)[C@@H]3CSSC[C@H](NC(=O)[C@H](CO)NC(=O)[C@H](CSSC[C@H](NC(=O)[C@H ](CC(=O)O)NC(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N)CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@ @H](CC4=CC=CC=C4)C(=O)N[C@H](C=O)CSSC[C@H](N)C(=O)N[C@H](C=O)CO)NC(=O)[C@@H](NC(=O)[C@@H](N)C(C)C)C(C)C)C(=O)N[C @@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)N[C@@H](CC4=CC=C(O)C=C4)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(C)C)C (=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](C)C(=O)N3)[C@@H]( C)CC)[C@@H](C)O)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC (=O)O)C(N)=O)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)N3CCC[C@H]3C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N [C@@H](CC(=O)O)C(=O)N[C@@H](CC3=CNC4=C3C=CC=C4)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@H]( 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deepchem.pdf

(=O)O)NC(=O)[C@H](CCCCN)NC1=O)C(C)C. N=C(N)NCCCCC(=O)O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OC1CCCCN1C1CCC(N[C@@H](O)[C@H]2CN(CC(F)F)C[C@@H] 2[C@@H](O)N[C@H]2CC[C@@H](Cl)CN2)[C@@H](F)C1. [Ca H2]. [Na H] failed sanitization [15:23:56] Explicit valence for atom # 6175 O, 3, is greater than permitted Mol Br. Br. Br. Br. Br. Br. Br. Br. Br. 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deepchem.pdf

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deepchem.pdf

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deepchem.pdf

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CC[C@H](C)[C@ H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O )[C@@H](N)CC(C)C)C(C)C)[C@@H](C)CC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CC1=CC =C(O)C=C1)C(=O)N[C@@H](CCCCN)C(=O)O)[C@@H](C)O. Cl. Cl. Cl. Cl. Cl. Cl. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCC O. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OC[C@H]1C[C@@H]2[C@@H](O)[C@@H](O)[C@@]1(O)CN2CC1CCCCC1. OC[C@H]1C[C@@H]2[C@@H](O)[C@@H](O)[C@@]1(O)CN2CC1CCCCC1 failed sanitization [15:24:08] Explicit valence for atom # 1064 O, 3, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H](

deepchem.pdf

NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O) [C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@ H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O) [C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]( NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](C)NC(=O)[C@H](CS)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C) NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)CNC (=O)[C@H](C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O )O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H]( CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C )NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O )[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC( C)C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H]( CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC (=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)CNC(=O)[C@H ](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O) [C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O) NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@ @H](NC(=O)[C@@H](N)CC(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C) C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)C C)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)NCC(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C )C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C (=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC1=CNC=N1)C1=OO1. 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CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O )[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO )NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC( =O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC (=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1= CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C @H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CS)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC (=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=C NC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)CNC(=O)[C@H](C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[ C@@H](NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[ C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC( =O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)N C(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O) [C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](C C(C)C)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CNC =N1)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H]1 CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC NC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N C(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CC(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C )CC)C(C)C)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C) O)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(=O)N[C@@H](CCC(N)=O )C(=O)NCC(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C) C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C( =O)N[C@@H](CC1=CNC=N1)C1=OO1. 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deepchem.pdf

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deepchem.pdf

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deepchem.pdf

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deepchem.pdf

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deepchem.pdf

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