woshixuhao
commited on
Commit
•
7daeb71
1
Parent(s):
c1888c9
Upload 3 files
Browse files- GeoGNN_model.pth +3 -0
- app.py +1610 -0
- column_descriptor.npy +3 -0
GeoGNN_model.pth
ADDED
@@ -0,0 +1,3 @@
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1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:3ff5277fd1a9269b3166882941438f3f68d43b2a8eefedf1d4b61b2801a66bf8
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3 |
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size 3139843
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app.py
ADDED
@@ -0,0 +1,1610 @@
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|
1 |
+
import torch
|
2 |
+
from torch_geometric.nn import MessagePassing
|
3 |
+
from compound_tools import *
|
4 |
+
from rdkit.Chem import Descriptors
|
5 |
+
from torch_geometric.data import Data
|
6 |
+
import argparse
|
7 |
+
import warnings
|
8 |
+
from rdkit.Chem.Descriptors import rdMolDescriptors
|
9 |
+
import pandas as pd
|
10 |
+
import os
|
11 |
+
from mordred import Calculator, descriptors, is_missing
|
12 |
+
from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
|
13 |
+
import torch.nn as nn
|
14 |
+
import torch.nn.functional as F
|
15 |
+
import numpy as np
|
16 |
+
from rdkit import Chem
|
17 |
+
from rdkit.Chem import AllChem
|
18 |
+
from rdkit.Chem import rdchem
|
19 |
+
import gradio as gr
|
20 |
+
DAY_LIGHT_FG_SMARTS_LIST = [
|
21 |
+
# C
|
22 |
+
"[CX4]",
|
23 |
+
"[$([CX2](=C)=C)]",
|
24 |
+
"[$([CX3]=[CX3])]",
|
25 |
+
"[$([CX2]#C)]",
|
26 |
+
# C & O
|
27 |
+
"[CX3]=[OX1]",
|
28 |
+
"[$([CX3]=[OX1]),$([CX3+]-[OX1-])]",
|
29 |
+
"[CX3](=[OX1])C",
|
30 |
+
"[OX1]=CN",
|
31 |
+
"[CX3](=[OX1])O",
|
32 |
+
"[CX3](=[OX1])[F,Cl,Br,I]",
|
33 |
+
"[CX3H1](=O)[#6]",
|
34 |
+
"[CX3](=[OX1])[OX2][CX3](=[OX1])",
|
35 |
+
"[NX3][CX3](=[OX1])[#6]",
|
36 |
+
"[NX3][CX3]=[NX3+]",
|
37 |
+
"[NX3,NX4+][CX3](=[OX1])[OX2,OX1-]",
|
38 |
+
"[NX3][CX3](=[OX1])[OX2H0]",
|
39 |
+
"[NX3,NX4+][CX3](=[OX1])[OX2H,OX1-]",
|
40 |
+
"[CX3](=O)[O-]",
|
41 |
+
"[CX3](=[OX1])(O)O",
|
42 |
+
"[CX3](=[OX1])([OX2])[OX2H,OX1H0-1]",
|
43 |
+
"C[OX2][CX3](=[OX1])[OX2]C",
|
44 |
+
"[CX3](=O)[OX2H1]",
|
45 |
+
"[CX3](=O)[OX1H0-,OX2H1]",
|
46 |
+
"[NX3][CX2]#[NX1]",
|
47 |
+
"[#6][CX3](=O)[OX2H0][#6]",
|
48 |
+
"[#6][CX3](=O)[#6]",
|
49 |
+
"[OD2]([#6])[#6]",
|
50 |
+
# H
|
51 |
+
"[H]",
|
52 |
+
"[!#1]",
|
53 |
+
"[H+]",
|
54 |
+
"[+H]",
|
55 |
+
"[!H]",
|
56 |
+
# N
|
57 |
+
"[NX3;H2,H1;!$(NC=O)]",
|
58 |
+
"[NX3][CX3]=[CX3]",
|
59 |
+
"[NX3;H2;!$(NC=[!#6]);!$(NC#[!#6])][#6]",
|
60 |
+
"[NX3;H2,H1;!$(NC=O)].[NX3;H2,H1;!$(NC=O)]",
|
61 |
+
"[NX3][$(C=C),$(cc)]",
|
62 |
+
"[NX3,NX4+][CX4H]([*])[CX3](=[OX1])[O,N]",
|
63 |
+
"[NX3H2,NH3X4+][CX4H]([*])[CX3](=[OX1])[NX3,NX4+][CX4H]([*])[CX3](=[OX1])[OX2H,OX1-]",
|
64 |
+
"[$([NX3H2,NX4H3+]),$([NX3H](C)(C))][CX4H]([*])[CX3](=[OX1])[OX2H,OX1-,N]",
|
65 |
+
"[CH3X4]",
|
66 |
+
"[CH2X4][CH2X4][CH2X4][NHX3][CH0X3](=[NH2X3+,NHX2+0])[NH2X3]",
|
67 |
+
"[CH2X4][CX3](=[OX1])[NX3H2]",
|
68 |
+
"[CH2X4][CX3](=[OX1])[OH0-,OH]",
|
69 |
+
"[CH2X4][SX2H,SX1H0-]",
|
70 |
+
"[CH2X4][CH2X4][CX3](=[OX1])[OH0-,OH]",
|
71 |
+
"[$([$([NX3H2,NX4H3+]),$([NX3H](C)(C))][CX4H2][CX3](=[OX1])[OX2H,OX1-,N])]",
|
72 |
+
"[CH2X4][#6X3]1:[$([#7X3H+,#7X2H0+0]:[#6X3H]:[#7X3H]),$([#7X3H])]:[#6X3H]:\
|
73 |
+
[$([#7X3H+,#7X2H0+0]:[#6X3H]:[#7X3H]),$([#7X3H])]:[#6X3H]1",
|
74 |
+
"[CHX4]([CH3X4])[CH2X4][CH3X4]",
|
75 |
+
"[CH2X4][CHX4]([CH3X4])[CH3X4]",
|
76 |
+
"[CH2X4][CH2X4][CH2X4][CH2X4][NX4+,NX3+0]",
|
77 |
+
"[CH2X4][CH2X4][SX2][CH3X4]",
|
78 |
+
"[CH2X4][cX3]1[cX3H][cX3H][cX3H][cX3H][cX3H]1",
|
79 |
+
"[$([NX3H,NX4H2+]),$([NX3](C)(C)(C))]1[CX4H]([CH2][CH2][CH2]1)[CX3](=[OX1])[OX2H,OX1-,N]",
|
80 |
+
"[CH2X4][OX2H]",
|
81 |
+
"[NX3][CX3]=[SX1]",
|
82 |
+
"[CHX4]([CH3X4])[OX2H]",
|
83 |
+
"[CH2X4][cX3]1[cX3H][nX3H][cX3]2[cX3H][cX3H][cX3H][cX3H][cX3]12",
|
84 |
+
"[CH2X4][cX3]1[cX3H][cX3H][cX3]([OHX2,OH0X1-])[cX3H][cX3H]1",
|
85 |
+
"[CHX4]([CH3X4])[CH3X4]",
|
86 |
+
"N[CX4H2][CX3](=[OX1])[O,N]",
|
87 |
+
"N1[CX4H]([CH2][CH2][CH2]1)[CX3](=[OX1])[O,N]",
|
88 |
+
"[$(*-[NX2-]-[NX2+]#[NX1]),$(*-[NX2]=[NX2+]=[NX1-])]",
|
89 |
+
"[$([NX1-]=[NX2+]=[NX1-]),$([NX1]#[NX2+]-[NX1-2])]",
|
90 |
+
"[#7]",
|
91 |
+
"[NX2]=N",
|
92 |
+
"[NX2]=[NX2]",
|
93 |
+
"[$([NX2]=[NX3+]([O-])[#6]),$([NX2]=[NX3+0](=[O])[#6])]",
|
94 |
+
"[$([#6]=[N+]=[N-]),$([#6-]-[N+]#[N])]",
|
95 |
+
"[$([nr5]:[nr5,or5,sr5]),$([nr5]:[cr5]:[nr5,or5,sr5])]",
|
96 |
+
"[NX3][NX3]",
|
97 |
+
"[NX3][NX2]=[*]",
|
98 |
+
"[CX3;$([C]([#6])[#6]),$([CH][#6])]=[NX2][#6]",
|
99 |
+
"[$([CX3]([#6])[#6]),$([CX3H][#6])]=[$([NX2][#6]),$([NX2H])]",
|
100 |
+
"[NX3+]=[CX3]",
|
101 |
+
"[CX3](=[OX1])[NX3H][CX3](=[OX1])",
|
102 |
+
"[CX3](=[OX1])[NX3H0]([#6])[CX3](=[OX1])",
|
103 |
+
"[CX3](=[OX1])[NX3H0]([NX3H0]([CX3](=[OX1]))[CX3](=[OX1]))[CX3](=[OX1])",
|
104 |
+
"[$([NX3](=[OX1])(=[OX1])O),$([NX3+]([OX1-])(=[OX1])O)]",
|
105 |
+
"[$([OX1]=[NX3](=[OX1])[OX1-]),$([OX1]=[NX3+]([OX1-])[OX1-])]",
|
106 |
+
"[NX1]#[CX2]",
|
107 |
+
"[CX1-]#[NX2+]",
|
108 |
+
"[$([NX3](=O)=O),$([NX3+](=O)[O-])][!#8]",
|
109 |
+
"[$([NX3](=O)=O),$([NX3+](=O)[O-])][!#8].[$([NX3](=O)=O),$([NX3+](=O)[O-])][!#8]",
|
110 |
+
"[NX2]=[OX1]",
|
111 |
+
"[$([#7+][OX1-]),$([#7v5]=[OX1]);!$([#7](~[O])~[O]);!$([#7]=[#7])]",
|
112 |
+
# O
|
113 |
+
"[OX2H]",
|
114 |
+
"[#6][OX2H]",
|
115 |
+
"[OX2H][CX3]=[OX1]",
|
116 |
+
"[OX2H]P",
|
117 |
+
"[OX2H][#6X3]=[#6]",
|
118 |
+
"[OX2H][cX3]:[c]",
|
119 |
+
"[OX2H][$(C=C),$(cc)]",
|
120 |
+
"[$([OH]-*=[!#6])]",
|
121 |
+
"[OX2,OX1-][OX2,OX1-]",
|
122 |
+
# P
|
123 |
+
"[$(P(=[OX1])([$([OX2H]),$([OX1-]),$([OX2]P)])([$([OX2H]),$([OX1-]),\
|
124 |
+
$([OX2]P)])[$([OX2H]),$([OX1-]),$([OX2]P)]),$([P+]([OX1-])([$([OX2H]),$([OX1-])\
|
125 |
+
,$([OX2]P)])([$([OX2H]),$([OX1-]),$([OX2]P)])[$([OX2H]),$([OX1-]),$([OX2]P)])]",
|
126 |
+
"[$(P(=[OX1])([OX2][#6])([$([OX2H]),$([OX1-]),$([OX2][#6])])[$([OX2H]),\
|
127 |
+
$([OX1-]),$([OX2][#6]),$([OX2]P)]),$([P+]([OX1-])([OX2][#6])([$([OX2H]),$([OX1-]),\
|
128 |
+
$([OX2][#6])])[$([OX2H]),$([OX1-]),$([OX2][#6]),$([OX2]P)])]",
|
129 |
+
# S
|
130 |
+
"[S-][CX3](=S)[#6]",
|
131 |
+
"[#6X3](=[SX1])([!N])[!N]",
|
132 |
+
"[SX2]",
|
133 |
+
"[#16X2H]",
|
134 |
+
"[#16!H0]",
|
135 |
+
"[#16X2H0]",
|
136 |
+
"[#16X2H0][!#16]",
|
137 |
+
"[#16X2H0][#16X2H0]",
|
138 |
+
"[#16X2H0][!#16].[#16X2H0][!#16]",
|
139 |
+
"[$([#16X3](=[OX1])[OX2H0]),$([#16X3+]([OX1-])[OX2H0])]",
|
140 |
+
"[$([#16X3](=[OX1])[OX2H,OX1H0-]),$([#16X3+]([OX1-])[OX2H,OX1H0-])]",
|
141 |
+
"[$([#16X4](=[OX1])=[OX1]),$([#16X4+2]([OX1-])[OX1-])]",
|
142 |
+
"[$([#16X4](=[OX1])(=[OX1])([#6])[#6]),$([#16X4+2]([OX1-])([OX1-])([#6])[#6])]",
|
143 |
+
"[$([#16X4](=[OX1])(=[OX1])([#6])[OX2H,OX1H0-]),$([#16X4+2]([OX1-])([OX1-])([#6])[OX2H,OX1H0-])]",
|
144 |
+
"[$([#16X4](=[OX1])(=[OX1])([#6])[OX2H0]),$([#16X4+2]([OX1-])([OX1-])([#6])[OX2H0])]",
|
145 |
+
"[$([#16X4]([NX3])(=[OX1])(=[OX1])[#6]),$([#16X4+2]([NX3])([OX1-])([OX1-])[#6])]",
|
146 |
+
"[SX4](C)(C)(=O)=N",
|
147 |
+
"[$([SX4](=[OX1])(=[OX1])([!O])[NX3]),$([SX4+2]([OX1-])([OX1-])([!O])[NX3])]",
|
148 |
+
"[$([#16X3]=[OX1]),$([#16X3+][OX1-])]",
|
149 |
+
"[$([#16X3](=[OX1])([#6])[#6]),$([#16X3+]([OX1-])([#6])[#6])]",
|
150 |
+
"[$([#16X4](=[OX1])(=[OX1])([OX2H,OX1H0-])[OX2][#6]),$([#16X4+2]([OX1-])([OX1-])([OX2H,OX1H0-])[OX2][#6])]",
|
151 |
+
"[$([SX4](=O)(=O)(O)O),$([SX4+2]([O-])([O-])(O)O)]",
|
152 |
+
"[$([#16X4](=[OX1])(=[OX1])([OX2][#6])[OX2][#6]),$([#16X4](=[OX1])(=[OX1])([OX2][#6])[OX2][#6])]",
|
153 |
+
"[$([#16X4]([NX3])(=[OX1])(=[OX1])[OX2][#6]),$([#16X4+2]([NX3])([OX1-])([OX1-])[OX2][#6])]",
|
154 |
+
"[$([#16X4]([NX3])(=[OX1])(=[OX1])[OX2H,OX1H0-]),$([#16X4+2]([NX3])([OX1-])([OX1-])[OX2H,OX1H0-])]",
|
155 |
+
"[#16X2][OX2H,OX1H0-]",
|
156 |
+
"[#16X2][OX2H0]",
|
157 |
+
# X
|
158 |
+
"[#6][F,Cl,Br,I]",
|
159 |
+
"[F,Cl,Br,I]",
|
160 |
+
"[F,Cl,Br,I].[F,Cl,Br,I].[F,Cl,Br,I]",
|
161 |
+
]
|
162 |
+
|
163 |
+
|
164 |
+
def get_gasteiger_partial_charges(mol, n_iter=12):
|
165 |
+
"""
|
166 |
+
Calculates list of gasteiger partial charges for each atom in mol object.
|
167 |
+
Args:
|
168 |
+
mol: rdkit mol object.
|
169 |
+
n_iter(int): number of iterations. Default 12.
|
170 |
+
Returns:
|
171 |
+
list of computed partial charges for each atom.
|
172 |
+
"""
|
173 |
+
Chem.rdPartialCharges.ComputeGasteigerCharges(mol, nIter=n_iter,
|
174 |
+
throwOnParamFailure=True)
|
175 |
+
partial_charges = [float(a.GetProp('_GasteigerCharge')) for a in
|
176 |
+
mol.GetAtoms()]
|
177 |
+
return partial_charges
|
178 |
+
|
179 |
+
|
180 |
+
def create_standardized_mol_id(smiles):
|
181 |
+
"""
|
182 |
+
Args:
|
183 |
+
smiles: smiles sequence.
|
184 |
+
Returns:
|
185 |
+
inchi.
|
186 |
+
"""
|
187 |
+
if check_smiles_validity(smiles):
|
188 |
+
# remove stereochemistry
|
189 |
+
smiles = AllChem.MolToSmiles(AllChem.MolFromSmiles(smiles),
|
190 |
+
isomericSmiles=False)
|
191 |
+
mol = AllChem.MolFromSmiles(smiles)
|
192 |
+
if not mol is None: # to catch weird issue with O=C1O[al]2oc(=O)c3ccc(cn3)c3ccccc3c3cccc(c3)c3ccccc3c3cc(C(F)(F)F)c(cc3o2)-c2ccccc2-c2cccc(c2)-c2ccccc2-c2cccnc21
|
193 |
+
if '.' in smiles: # if multiple species, pick largest molecule
|
194 |
+
mol_species_list = split_rdkit_mol_obj(mol)
|
195 |
+
largest_mol = get_largest_mol(mol_species_list)
|
196 |
+
inchi = AllChem.MolToInchi(largest_mol)
|
197 |
+
else:
|
198 |
+
inchi = AllChem.MolToInchi(mol)
|
199 |
+
return inchi
|
200 |
+
else:
|
201 |
+
return
|
202 |
+
else:
|
203 |
+
return
|
204 |
+
|
205 |
+
|
206 |
+
def check_smiles_validity(smiles):
|
207 |
+
"""
|
208 |
+
Check whether the smile can't be converted to rdkit mol object.
|
209 |
+
"""
|
210 |
+
try:
|
211 |
+
m = Chem.MolFromSmiles(smiles)
|
212 |
+
if m:
|
213 |
+
return True
|
214 |
+
else:
|
215 |
+
return False
|
216 |
+
except Exception as e:
|
217 |
+
return False
|
218 |
+
|
219 |
+
|
220 |
+
def split_rdkit_mol_obj(mol):
|
221 |
+
"""
|
222 |
+
Split rdkit mol object containing multiple species or one species into a
|
223 |
+
list of mol objects or a list containing a single object respectively.
|
224 |
+
Args:
|
225 |
+
mol: rdkit mol object.
|
226 |
+
"""
|
227 |
+
smiles = AllChem.MolToSmiles(mol, isomericSmiles=True)
|
228 |
+
smiles_list = smiles.split('.')
|
229 |
+
mol_species_list = []
|
230 |
+
for s in smiles_list:
|
231 |
+
if check_smiles_validity(s):
|
232 |
+
mol_species_list.append(AllChem.MolFromSmiles(s))
|
233 |
+
return mol_species_list
|
234 |
+
|
235 |
+
|
236 |
+
def get_largest_mol(mol_list):
|
237 |
+
"""
|
238 |
+
Given a list of rdkit mol objects, returns mol object containing the
|
239 |
+
largest num of atoms. If multiple containing largest num of atoms,
|
240 |
+
picks the first one.
|
241 |
+
Args:
|
242 |
+
mol_list(list): a list of rdkit mol object.
|
243 |
+
Returns:
|
244 |
+
the largest mol.
|
245 |
+
"""
|
246 |
+
num_atoms_list = [len(m.GetAtoms()) for m in mol_list]
|
247 |
+
largest_mol_idx = num_atoms_list.index(max(num_atoms_list))
|
248 |
+
return mol_list[largest_mol_idx]
|
249 |
+
|
250 |
+
|
251 |
+
def rdchem_enum_to_list(values):
|
252 |
+
"""values = {0: rdkit.Chem.rdchem.ChiralType.CHI_UNSPECIFIED,
|
253 |
+
1: rdkit.Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW,
|
254 |
+
2: rdkit.Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW,
|
255 |
+
3: rdkit.Chem.rdchem.ChiralType.CHI_OTHER}
|
256 |
+
"""
|
257 |
+
return [values[i] for i in range(len(values))]
|
258 |
+
|
259 |
+
|
260 |
+
def safe_index(alist, elem):
|
261 |
+
"""
|
262 |
+
Return index of element e in list l. If e is not present, return the last index
|
263 |
+
"""
|
264 |
+
try:
|
265 |
+
return alist.index(elem)
|
266 |
+
except ValueError:
|
267 |
+
return len(alist) - 1
|
268 |
+
|
269 |
+
|
270 |
+
def get_atom_feature_dims(list_acquired_feature_names):
|
271 |
+
""" tbd
|
272 |
+
"""
|
273 |
+
return list(map(len, [CompoundKit.atom_vocab_dict[name] for name in list_acquired_feature_names]))
|
274 |
+
|
275 |
+
|
276 |
+
def get_bond_feature_dims(list_acquired_feature_names):
|
277 |
+
""" tbd
|
278 |
+
"""
|
279 |
+
list_bond_feat_dim = list(map(len, [CompoundKit.bond_vocab_dict[name] for name in list_acquired_feature_names]))
|
280 |
+
# +1 for self loop edges
|
281 |
+
return [_l + 1 for _l in list_bond_feat_dim]
|
282 |
+
|
283 |
+
|
284 |
+
class CompoundKit(object):
|
285 |
+
"""
|
286 |
+
CompoundKit
|
287 |
+
"""
|
288 |
+
atom_vocab_dict = {
|
289 |
+
"atomic_num": list(range(1, 119)) + ['misc'],
|
290 |
+
"chiral_tag": rdchem_enum_to_list(rdchem.ChiralType.values),
|
291 |
+
"degree": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'],
|
292 |
+
"explicit_valence": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 'misc'],
|
293 |
+
"formal_charge": [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'],
|
294 |
+
"hybridization": rdchem_enum_to_list(rdchem.HybridizationType.values),
|
295 |
+
"implicit_valence": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 'misc'],
|
296 |
+
"is_aromatic": [0, 1],
|
297 |
+
"total_numHs": [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
298 |
+
'num_radical_e': [0, 1, 2, 3, 4, 'misc'],
|
299 |
+
'atom_is_in_ring': [0, 1],
|
300 |
+
'valence_out_shell': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
301 |
+
'in_num_ring_with_size3': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
302 |
+
'in_num_ring_with_size4': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
303 |
+
'in_num_ring_with_size5': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
304 |
+
'in_num_ring_with_size6': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
305 |
+
'in_num_ring_with_size7': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
306 |
+
'in_num_ring_with_size8': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
|
307 |
+
}
|
308 |
+
bond_vocab_dict = {
|
309 |
+
"bond_dir": rdchem_enum_to_list(rdchem.BondDir.values),
|
310 |
+
"bond_type": rdchem_enum_to_list(rdchem.BondType.values),
|
311 |
+
"is_in_ring": [0, 1],
|
312 |
+
|
313 |
+
'bond_stereo': rdchem_enum_to_list(rdchem.BondStereo.values),
|
314 |
+
'is_conjugated': [0, 1],
|
315 |
+
}
|
316 |
+
# float features
|
317 |
+
atom_float_names = ["van_der_waals_radis", "partial_charge", 'mass']
|
318 |
+
# bond_float_feats= ["bond_length", "bond_angle"] # optional
|
319 |
+
|
320 |
+
### functional groups
|
321 |
+
day_light_fg_smarts_list = DAY_LIGHT_FG_SMARTS_LIST
|
322 |
+
day_light_fg_mo_list = [Chem.MolFromSmarts(smarts) for smarts in day_light_fg_smarts_list]
|
323 |
+
|
324 |
+
morgan_fp_N = 200
|
325 |
+
morgan2048_fp_N = 2048
|
326 |
+
maccs_fp_N = 167
|
327 |
+
|
328 |
+
period_table = Chem.GetPeriodicTable()
|
329 |
+
|
330 |
+
### atom
|
331 |
+
|
332 |
+
@staticmethod
|
333 |
+
def get_atom_value(atom, name):
|
334 |
+
"""get atom values"""
|
335 |
+
if name == 'atomic_num':
|
336 |
+
return atom.GetAtomicNum()
|
337 |
+
elif name == 'chiral_tag':
|
338 |
+
return atom.GetChiralTag()
|
339 |
+
elif name == 'degree':
|
340 |
+
return atom.GetDegree()
|
341 |
+
elif name == 'explicit_valence':
|
342 |
+
return atom.GetExplicitValence()
|
343 |
+
elif name == 'formal_charge':
|
344 |
+
return atom.GetFormalCharge()
|
345 |
+
elif name == 'hybridization':
|
346 |
+
return atom.GetHybridization()
|
347 |
+
elif name == 'implicit_valence':
|
348 |
+
return atom.GetImplicitValence()
|
349 |
+
elif name == 'is_aromatic':
|
350 |
+
return int(atom.GetIsAromatic())
|
351 |
+
elif name == 'mass':
|
352 |
+
return int(atom.GetMass())
|
353 |
+
elif name == 'total_numHs':
|
354 |
+
return atom.GetTotalNumHs()
|
355 |
+
elif name == 'num_radical_e':
|
356 |
+
return atom.GetNumRadicalElectrons()
|
357 |
+
elif name == 'atom_is_in_ring':
|
358 |
+
return int(atom.IsInRing())
|
359 |
+
elif name == 'valence_out_shell':
|
360 |
+
return CompoundKit.period_table.GetNOuterElecs(atom.GetAtomicNum())
|
361 |
+
else:
|
362 |
+
raise ValueError(name)
|
363 |
+
|
364 |
+
@staticmethod
|
365 |
+
def get_atom_feature_id(atom, name):
|
366 |
+
"""get atom features id"""
|
367 |
+
assert name in CompoundKit.atom_vocab_dict, "%s not found in atom_vocab_dict" % name
|
368 |
+
return safe_index(CompoundKit.atom_vocab_dict[name], CompoundKit.get_atom_value(atom, name))
|
369 |
+
|
370 |
+
@staticmethod
|
371 |
+
def get_atom_feature_size(name):
|
372 |
+
"""get atom features size"""
|
373 |
+
assert name in CompoundKit.atom_vocab_dict, "%s not found in atom_vocab_dict" % name
|
374 |
+
return len(CompoundKit.atom_vocab_dict[name])
|
375 |
+
|
376 |
+
### bond
|
377 |
+
|
378 |
+
@staticmethod
|
379 |
+
def get_bond_value(bond, name):
|
380 |
+
"""get bond values"""
|
381 |
+
if name == 'bond_dir':
|
382 |
+
return bond.GetBondDir()
|
383 |
+
elif name == 'bond_type':
|
384 |
+
return bond.GetBondType()
|
385 |
+
elif name == 'is_in_ring':
|
386 |
+
return int(bond.IsInRing())
|
387 |
+
elif name == 'is_conjugated':
|
388 |
+
return int(bond.GetIsConjugated())
|
389 |
+
elif name == 'bond_stereo':
|
390 |
+
return bond.GetStereo()
|
391 |
+
else:
|
392 |
+
raise ValueError(name)
|
393 |
+
|
394 |
+
@staticmethod
|
395 |
+
def get_bond_feature_id(bond, name):
|
396 |
+
"""get bond features id"""
|
397 |
+
assert name in CompoundKit.bond_vocab_dict, "%s not found in bond_vocab_dict" % name
|
398 |
+
return safe_index(CompoundKit.bond_vocab_dict[name], CompoundKit.get_bond_value(bond, name))
|
399 |
+
|
400 |
+
@staticmethod
|
401 |
+
def get_bond_feature_size(name):
|
402 |
+
"""get bond features size"""
|
403 |
+
assert name in CompoundKit.bond_vocab_dict, "%s not found in bond_vocab_dict" % name
|
404 |
+
return len(CompoundKit.bond_vocab_dict[name])
|
405 |
+
|
406 |
+
### fingerprint
|
407 |
+
|
408 |
+
@staticmethod
|
409 |
+
def get_morgan_fingerprint(mol, radius=2):
|
410 |
+
"""get morgan fingerprint"""
|
411 |
+
nBits = CompoundKit.morgan_fp_N
|
412 |
+
mfp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nBits)
|
413 |
+
return [int(b) for b in mfp.ToBitString()]
|
414 |
+
|
415 |
+
@staticmethod
|
416 |
+
def get_morgan2048_fingerprint(mol, radius=2):
|
417 |
+
"""get morgan2048 fingerprint"""
|
418 |
+
nBits = CompoundKit.morgan2048_fp_N
|
419 |
+
mfp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nBits)
|
420 |
+
return [int(b) for b in mfp.ToBitString()]
|
421 |
+
|
422 |
+
@staticmethod
|
423 |
+
def get_maccs_fingerprint(mol):
|
424 |
+
"""get maccs fingerprint"""
|
425 |
+
fp = AllChem.GetMACCSKeysFingerprint(mol)
|
426 |
+
return [int(b) for b in fp.ToBitString()]
|
427 |
+
|
428 |
+
### functional groups
|
429 |
+
|
430 |
+
@staticmethod
|
431 |
+
def get_daylight_functional_group_counts(mol):
|
432 |
+
"""get daylight functional group counts"""
|
433 |
+
fg_counts = []
|
434 |
+
for fg_mol in CompoundKit.day_light_fg_mo_list:
|
435 |
+
sub_structs = Chem.Mol.GetSubstructMatches(mol, fg_mol, uniquify=True)
|
436 |
+
fg_counts.append(len(sub_structs))
|
437 |
+
return fg_counts
|
438 |
+
|
439 |
+
@staticmethod
|
440 |
+
def get_ring_size(mol):
|
441 |
+
"""return (N,6) list"""
|
442 |
+
rings = mol.GetRingInfo()
|
443 |
+
rings_info = []
|
444 |
+
for r in rings.AtomRings():
|
445 |
+
rings_info.append(r)
|
446 |
+
ring_list = []
|
447 |
+
for atom in mol.GetAtoms():
|
448 |
+
atom_result = []
|
449 |
+
for ringsize in range(3, 9):
|
450 |
+
num_of_ring_at_ringsize = 0
|
451 |
+
for r in rings_info:
|
452 |
+
if len(r) == ringsize and atom.GetIdx() in r:
|
453 |
+
num_of_ring_at_ringsize += 1
|
454 |
+
if num_of_ring_at_ringsize > 8:
|
455 |
+
num_of_ring_at_ringsize = 9
|
456 |
+
atom_result.append(num_of_ring_at_ringsize)
|
457 |
+
|
458 |
+
ring_list.append(atom_result)
|
459 |
+
return ring_list
|
460 |
+
|
461 |
+
@staticmethod
|
462 |
+
def atom_to_feat_vector(atom):
|
463 |
+
""" tbd """
|
464 |
+
atom_names = {
|
465 |
+
"atomic_num": safe_index(CompoundKit.atom_vocab_dict["atomic_num"], atom.GetAtomicNum()),
|
466 |
+
"chiral_tag": safe_index(CompoundKit.atom_vocab_dict["chiral_tag"], atom.GetChiralTag()),
|
467 |
+
"degree": safe_index(CompoundKit.atom_vocab_dict["degree"], atom.GetTotalDegree()),
|
468 |
+
"explicit_valence": safe_index(CompoundKit.atom_vocab_dict["explicit_valence"], atom.GetExplicitValence()),
|
469 |
+
"formal_charge": safe_index(CompoundKit.atom_vocab_dict["formal_charge"], atom.GetFormalCharge()),
|
470 |
+
"hybridization": safe_index(CompoundKit.atom_vocab_dict["hybridization"], atom.GetHybridization()),
|
471 |
+
"implicit_valence": safe_index(CompoundKit.atom_vocab_dict["implicit_valence"], atom.GetImplicitValence()),
|
472 |
+
"is_aromatic": safe_index(CompoundKit.atom_vocab_dict["is_aromatic"], int(atom.GetIsAromatic())),
|
473 |
+
"total_numHs": safe_index(CompoundKit.atom_vocab_dict["total_numHs"], atom.GetTotalNumHs()),
|
474 |
+
'num_radical_e': safe_index(CompoundKit.atom_vocab_dict['num_radical_e'], atom.GetNumRadicalElectrons()),
|
475 |
+
'atom_is_in_ring': safe_index(CompoundKit.atom_vocab_dict['atom_is_in_ring'], int(atom.IsInRing())),
|
476 |
+
'valence_out_shell': safe_index(CompoundKit.atom_vocab_dict['valence_out_shell'],
|
477 |
+
CompoundKit.period_table.GetNOuterElecs(atom.GetAtomicNum())),
|
478 |
+
'van_der_waals_radis': CompoundKit.period_table.GetRvdw(atom.GetAtomicNum()),
|
479 |
+
'partial_charge': CompoundKit.check_partial_charge(atom),
|
480 |
+
'mass': atom.GetMass(),
|
481 |
+
}
|
482 |
+
return atom_names
|
483 |
+
|
484 |
+
@staticmethod
|
485 |
+
def get_atom_names(mol):
|
486 |
+
"""get atom name list
|
487 |
+
TODO: to be remove in the future
|
488 |
+
"""
|
489 |
+
atom_features_dicts = []
|
490 |
+
Chem.rdPartialCharges.ComputeGasteigerCharges(mol)
|
491 |
+
for i, atom in enumerate(mol.GetAtoms()):
|
492 |
+
atom_features_dicts.append(CompoundKit.atom_to_feat_vector(atom))
|
493 |
+
|
494 |
+
ring_list = CompoundKit.get_ring_size(mol)
|
495 |
+
for i, atom in enumerate(mol.GetAtoms()):
|
496 |
+
atom_features_dicts[i]['in_num_ring_with_size3'] = safe_index(
|
497 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size3'], ring_list[i][0])
|
498 |
+
atom_features_dicts[i]['in_num_ring_with_size4'] = safe_index(
|
499 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size4'], ring_list[i][1])
|
500 |
+
atom_features_dicts[i]['in_num_ring_with_size5'] = safe_index(
|
501 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size5'], ring_list[i][2])
|
502 |
+
atom_features_dicts[i]['in_num_ring_with_size6'] = safe_index(
|
503 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size6'], ring_list[i][3])
|
504 |
+
atom_features_dicts[i]['in_num_ring_with_size7'] = safe_index(
|
505 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size7'], ring_list[i][4])
|
506 |
+
atom_features_dicts[i]['in_num_ring_with_size8'] = safe_index(
|
507 |
+
CompoundKit.atom_vocab_dict['in_num_ring_with_size8'], ring_list[i][5])
|
508 |
+
|
509 |
+
return atom_features_dicts
|
510 |
+
|
511 |
+
@staticmethod
|
512 |
+
def check_partial_charge(atom):
|
513 |
+
"""tbd"""
|
514 |
+
pc = atom.GetDoubleProp('_GasteigerCharge')
|
515 |
+
if pc != pc:
|
516 |
+
# unsupported atom, replace nan with 0
|
517 |
+
pc = 0
|
518 |
+
if pc == float('inf'):
|
519 |
+
# max 4 for other atoms, set to 10 here if inf is get
|
520 |
+
pc = 10
|
521 |
+
return pc
|
522 |
+
|
523 |
+
|
524 |
+
class Compound3DKit(object):
|
525 |
+
"""the 3Dkit of Compound"""
|
526 |
+
|
527 |
+
@staticmethod
|
528 |
+
def get_atom_poses(mol, conf):
|
529 |
+
"""tbd"""
|
530 |
+
atom_poses = []
|
531 |
+
for i, atom in enumerate(mol.GetAtoms()):
|
532 |
+
if atom.GetAtomicNum() == 0:
|
533 |
+
return [[0.0, 0.0, 0.0]] * len(mol.GetAtoms())
|
534 |
+
pos = conf.GetAtomPosition(i)
|
535 |
+
atom_poses.append([pos.x, pos.y, pos.z])
|
536 |
+
return atom_poses
|
537 |
+
|
538 |
+
@staticmethod
|
539 |
+
def get_MMFF_atom_poses(mol, numConfs=None, return_energy=False):
|
540 |
+
"""the atoms of mol will be changed in some cases."""
|
541 |
+
conf = mol.GetConformer()
|
542 |
+
atom_poses = Compound3DKit.get_atom_poses(mol, conf)
|
543 |
+
return mol,atom_poses
|
544 |
+
# try:
|
545 |
+
# new_mol = Chem.AddHs(mol)
|
546 |
+
# res = AllChem.EmbedMultipleConfs(new_mol, numConfs=numConfs)
|
547 |
+
# ### MMFF generates multiple conformations
|
548 |
+
# res = AllChem.MMFFOptimizeMoleculeConfs(new_mol)
|
549 |
+
# new_mol = Chem.RemoveHs(new_mol)
|
550 |
+
# index = np.argmin([x[1] for x in res])
|
551 |
+
# energy = res[index][1]
|
552 |
+
# conf = new_mol.GetConformer(id=int(index))
|
553 |
+
# except:
|
554 |
+
# new_mol = mol
|
555 |
+
# AllChem.Compute2DCoords(new_mol)
|
556 |
+
# energy = 0
|
557 |
+
# conf = new_mol.GetConformer()
|
558 |
+
#
|
559 |
+
# atom_poses = Compound3DKit.get_atom_poses(new_mol, conf)
|
560 |
+
# if return_energy:
|
561 |
+
# return new_mol, atom_poses, energy
|
562 |
+
# else:
|
563 |
+
# return new_mol, atom_poses
|
564 |
+
|
565 |
+
@staticmethod
|
566 |
+
def get_2d_atom_poses(mol):
|
567 |
+
"""get 2d atom poses"""
|
568 |
+
AllChem.Compute2DCoords(mol)
|
569 |
+
conf = mol.GetConformer()
|
570 |
+
atom_poses = Compound3DKit.get_atom_poses(mol, conf)
|
571 |
+
return atom_poses
|
572 |
+
|
573 |
+
@staticmethod
|
574 |
+
def get_bond_lengths(edges, atom_poses):
|
575 |
+
"""get bond lengths"""
|
576 |
+
bond_lengths = []
|
577 |
+
for src_node_i, tar_node_j in edges:
|
578 |
+
bond_lengths.append(np.linalg.norm(atom_poses[tar_node_j] - atom_poses[src_node_i]))
|
579 |
+
bond_lengths = np.array(bond_lengths, 'float32')
|
580 |
+
return bond_lengths
|
581 |
+
|
582 |
+
@staticmethod
|
583 |
+
def get_superedge_angles(edges, atom_poses, dir_type='HT'):
|
584 |
+
"""get superedge angles"""
|
585 |
+
|
586 |
+
def _get_vec(atom_poses, edge):
|
587 |
+
return atom_poses[edge[1]] - atom_poses[edge[0]]
|
588 |
+
|
589 |
+
def _get_angle(vec1, vec2):
|
590 |
+
norm1 = np.linalg.norm(vec1)
|
591 |
+
norm2 = np.linalg.norm(vec2)
|
592 |
+
if norm1 == 0 or norm2 == 0:
|
593 |
+
return 0
|
594 |
+
vec1 = vec1 / (norm1 + 1e-5) # 1e-5: prevent numerical errors
|
595 |
+
vec2 = vec2 / (norm2 + 1e-5)
|
596 |
+
angle = np.arccos(np.dot(vec1, vec2))
|
597 |
+
return angle
|
598 |
+
|
599 |
+
E = len(edges)
|
600 |
+
edge_indices = np.arange(E)
|
601 |
+
super_edges = []
|
602 |
+
bond_angles = []
|
603 |
+
bond_angle_dirs = []
|
604 |
+
for tar_edge_i in range(E):
|
605 |
+
tar_edge = edges[tar_edge_i]
|
606 |
+
if dir_type == 'HT':
|
607 |
+
src_edge_indices = edge_indices[edges[:, 1] == tar_edge[0]]
|
608 |
+
elif dir_type == 'HH':
|
609 |
+
src_edge_indices = edge_indices[edges[:, 1] == tar_edge[1]]
|
610 |
+
else:
|
611 |
+
raise ValueError(dir_type)
|
612 |
+
for src_edge_i in src_edge_indices:
|
613 |
+
if src_edge_i == tar_edge_i:
|
614 |
+
continue
|
615 |
+
src_edge = edges[src_edge_i]
|
616 |
+
src_vec = _get_vec(atom_poses, src_edge)
|
617 |
+
tar_vec = _get_vec(atom_poses, tar_edge)
|
618 |
+
super_edges.append([src_edge_i, tar_edge_i])
|
619 |
+
angle = _get_angle(src_vec, tar_vec)
|
620 |
+
bond_angles.append(angle)
|
621 |
+
bond_angle_dirs.append(src_edge[1] == tar_edge[0]) # H -> H or H -> T
|
622 |
+
|
623 |
+
if len(super_edges) == 0:
|
624 |
+
super_edges = np.zeros([0, 2], 'int64')
|
625 |
+
bond_angles = np.zeros([0, ], 'float32')
|
626 |
+
else:
|
627 |
+
super_edges = np.array(super_edges, 'int64')
|
628 |
+
bond_angles = np.array(bond_angles, 'float32')
|
629 |
+
return super_edges, bond_angles, bond_angle_dirs
|
630 |
+
|
631 |
+
|
632 |
+
def new_smiles_to_graph_data(smiles, **kwargs):
|
633 |
+
"""
|
634 |
+
Convert smiles to graph data.
|
635 |
+
"""
|
636 |
+
mol = AllChem.MolFromSmiles(smiles)
|
637 |
+
if mol is None:
|
638 |
+
return None
|
639 |
+
data = new_mol_to_graph_data(mol)
|
640 |
+
return data
|
641 |
+
|
642 |
+
|
643 |
+
def new_mol_to_graph_data(mol):
|
644 |
+
"""
|
645 |
+
mol_to_graph_data
|
646 |
+
Args:
|
647 |
+
atom_features: Atom features.
|
648 |
+
edge_features: Edge features.
|
649 |
+
morgan_fingerprint: Morgan fingerprint.
|
650 |
+
functional_groups: Functional groups.
|
651 |
+
"""
|
652 |
+
if len(mol.GetAtoms()) == 0:
|
653 |
+
return None
|
654 |
+
|
655 |
+
atom_id_names = list(CompoundKit.atom_vocab_dict.keys()) + CompoundKit.atom_float_names
|
656 |
+
bond_id_names = list(CompoundKit.bond_vocab_dict.keys())
|
657 |
+
|
658 |
+
data = {}
|
659 |
+
|
660 |
+
### atom features
|
661 |
+
data = {name: [] for name in atom_id_names}
|
662 |
+
|
663 |
+
raw_atom_feat_dicts = CompoundKit.get_atom_names(mol)
|
664 |
+
for atom_feat in raw_atom_feat_dicts:
|
665 |
+
for name in atom_id_names:
|
666 |
+
data[name].append(atom_feat[name])
|
667 |
+
|
668 |
+
### bond and bond features
|
669 |
+
for name in bond_id_names:
|
670 |
+
data[name] = []
|
671 |
+
data['edges'] = []
|
672 |
+
|
673 |
+
for bond in mol.GetBonds():
|
674 |
+
i = bond.GetBeginAtomIdx()
|
675 |
+
j = bond.GetEndAtomIdx()
|
676 |
+
# i->j and j->i
|
677 |
+
data['edges'] += [(i, j), (j, i)]
|
678 |
+
for name in bond_id_names:
|
679 |
+
bond_feature_id = CompoundKit.get_bond_feature_id(bond, name)
|
680 |
+
data[name] += [bond_feature_id] * 2
|
681 |
+
|
682 |
+
#### self loop
|
683 |
+
N = len(data[atom_id_names[0]])
|
684 |
+
for i in range(N):
|
685 |
+
data['edges'] += [(i, i)]
|
686 |
+
for name in bond_id_names:
|
687 |
+
bond_feature_id = get_bond_feature_dims([name])[0] - 1 # self loop: value = len - 1
|
688 |
+
data[name] += [bond_feature_id] * N
|
689 |
+
|
690 |
+
### make ndarray and check length
|
691 |
+
for name in list(CompoundKit.atom_vocab_dict.keys()):
|
692 |
+
data[name] = np.array(data[name], 'int64')
|
693 |
+
for name in CompoundKit.atom_float_names:
|
694 |
+
data[name] = np.array(data[name], 'float32')
|
695 |
+
for name in bond_id_names:
|
696 |
+
data[name] = np.array(data[name], 'int64')
|
697 |
+
data['edges'] = np.array(data['edges'], 'int64')
|
698 |
+
|
699 |
+
### morgan fingerprint
|
700 |
+
data['morgan_fp'] = np.array(CompoundKit.get_morgan_fingerprint(mol), 'int64')
|
701 |
+
# data['morgan2048_fp'] = np.array(CompoundKit.get_morgan2048_fingerprint(mol), 'int64')
|
702 |
+
data['maccs_fp'] = np.array(CompoundKit.get_maccs_fingerprint(mol), 'int64')
|
703 |
+
data['daylight_fg_counts'] = np.array(CompoundKit.get_daylight_functional_group_counts(mol), 'int64')
|
704 |
+
return data
|
705 |
+
|
706 |
+
|
707 |
+
def mol_to_graph_data(mol):
|
708 |
+
"""
|
709 |
+
mol_to_graph_data
|
710 |
+
Args:
|
711 |
+
atom_features: Atom features.
|
712 |
+
edge_features: Edge features.
|
713 |
+
morgan_fingerprint: Morgan fingerprint.
|
714 |
+
functional_groups: Functional groups.
|
715 |
+
"""
|
716 |
+
if len(mol.GetAtoms()) == 0:
|
717 |
+
return None
|
718 |
+
|
719 |
+
atom_id_names = [
|
720 |
+
"atomic_num", "chiral_tag", "degree", "explicit_valence",
|
721 |
+
"formal_charge", "hybridization", "implicit_valence",
|
722 |
+
"is_aromatic", "total_numHs",
|
723 |
+
]
|
724 |
+
bond_id_names = [
|
725 |
+
"bond_dir", "bond_type", "is_in_ring",
|
726 |
+
]
|
727 |
+
|
728 |
+
data = {}
|
729 |
+
for name in atom_id_names:
|
730 |
+
data[name] = []
|
731 |
+
data['mass'] = []
|
732 |
+
for name in bond_id_names:
|
733 |
+
data[name] = []
|
734 |
+
data['edges'] = []
|
735 |
+
|
736 |
+
### atom features
|
737 |
+
for i, atom in enumerate(mol.GetAtoms()):
|
738 |
+
if atom.GetAtomicNum() == 0:
|
739 |
+
return None
|
740 |
+
for name in atom_id_names:
|
741 |
+
data[name].append(CompoundKit.get_atom_feature_id(atom, name) + 1) # 0: OOV
|
742 |
+
data['mass'].append(CompoundKit.get_atom_value(atom, 'mass') * 0.01)
|
743 |
+
|
744 |
+
### bond features
|
745 |
+
for bond in mol.GetBonds():
|
746 |
+
i = bond.GetBeginAtomIdx()
|
747 |
+
j = bond.GetEndAtomIdx()
|
748 |
+
# i->j and j->i
|
749 |
+
data['edges'] += [(i, j), (j, i)]
|
750 |
+
for name in bond_id_names:
|
751 |
+
bond_feature_id = CompoundKit.get_bond_feature_id(bond, name) + 1 # 0: OOV
|
752 |
+
data[name] += [bond_feature_id] * 2
|
753 |
+
|
754 |
+
### self loop (+2)
|
755 |
+
N = len(data[atom_id_names[0]])
|
756 |
+
for i in range(N):
|
757 |
+
data['edges'] += [(i, i)]
|
758 |
+
for name in bond_id_names:
|
759 |
+
bond_feature_id = CompoundKit.get_bond_feature_size(name) + 2 # N + 2: self loop
|
760 |
+
data[name] += [bond_feature_id] * N
|
761 |
+
|
762 |
+
### check whether edge exists
|
763 |
+
if len(data['edges']) == 0: # mol has no bonds
|
764 |
+
for name in bond_id_names:
|
765 |
+
data[name] = np.zeros((0,), dtype="int64")
|
766 |
+
data['edges'] = np.zeros((0, 2), dtype="int64")
|
767 |
+
|
768 |
+
### make ndarray and check length
|
769 |
+
for name in atom_id_names:
|
770 |
+
data[name] = np.array(data[name], 'int64')
|
771 |
+
data['mass'] = np.array(data['mass'], 'float32')
|
772 |
+
for name in bond_id_names:
|
773 |
+
data[name] = np.array(data[name], 'int64')
|
774 |
+
data['edges'] = np.array(data['edges'], 'int64')
|
775 |
+
|
776 |
+
### morgan fingerprint
|
777 |
+
data['morgan_fp'] = np.array(CompoundKit.get_morgan_fingerprint(mol), 'int64')
|
778 |
+
# data['morgan2048_fp'] = np.array(CompoundKit.get_morgan2048_fingerprint(mol), 'int64')
|
779 |
+
data['maccs_fp'] = np.array(CompoundKit.get_maccs_fingerprint(mol), 'int64')
|
780 |
+
data['daylight_fg_counts'] = np.array(CompoundKit.get_daylight_functional_group_counts(mol), 'int64')
|
781 |
+
return data
|
782 |
+
|
783 |
+
|
784 |
+
def mol_to_geognn_graph_data(mol, atom_poses, dir_type):
|
785 |
+
"""
|
786 |
+
mol: rdkit molecule
|
787 |
+
dir_type: direction type for bond_angle grpah
|
788 |
+
"""
|
789 |
+
if len(mol.GetAtoms()) == 0:
|
790 |
+
return None
|
791 |
+
|
792 |
+
data = mol_to_graph_data(mol)
|
793 |
+
|
794 |
+
data['atom_pos'] = np.array(atom_poses, 'float32')
|
795 |
+
data['bond_length'] = Compound3DKit.get_bond_lengths(data['edges'], data['atom_pos'])
|
796 |
+
BondAngleGraph_edges, bond_angles, bond_angle_dirs = \
|
797 |
+
Compound3DKit.get_superedge_angles(data['edges'], data['atom_pos'])
|
798 |
+
data['BondAngleGraph_edges'] = BondAngleGraph_edges
|
799 |
+
data['bond_angle'] = np.array(bond_angles, 'float32')
|
800 |
+
return data
|
801 |
+
|
802 |
+
|
803 |
+
def mol_to_geognn_graph_data_MMFF3d(mol):
|
804 |
+
"""tbd"""
|
805 |
+
if len(mol.GetAtoms()) <= 400:
|
806 |
+
mol, atom_poses = Compound3DKit.get_MMFF_atom_poses(mol, numConfs=10)
|
807 |
+
else:
|
808 |
+
atom_poses = Compound3DKit.get_2d_atom_poses(mol)
|
809 |
+
return mol_to_geognn_graph_data(mol, atom_poses, dir_type='HT')
|
810 |
+
|
811 |
+
|
812 |
+
def mol_to_geognn_graph_data_raw3d(mol):
|
813 |
+
"""tbd"""
|
814 |
+
atom_poses = Compound3DKit.get_atom_poses(mol, mol.GetConformer())
|
815 |
+
return mol_to_geognn_graph_data(mol, atom_poses, dir_type='HT')
|
816 |
+
|
817 |
+
def obtain_3D_mol(smiles,name):
|
818 |
+
mol = AllChem.MolFromSmiles(smiles)
|
819 |
+
new_mol = Chem.AddHs(mol)
|
820 |
+
res = AllChem.EmbedMultipleConfs(new_mol)
|
821 |
+
### MMFF generates multiple conformations
|
822 |
+
res = AllChem.MMFFOptimizeMoleculeConfs(new_mol)
|
823 |
+
new_mol = Chem.RemoveHs(new_mol)
|
824 |
+
Chem.MolToMolFile(new_mol, name+'.mol')
|
825 |
+
return new_mol
|
826 |
+
|
827 |
+
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
|
828 |
+
warnings.filterwarnings('ignore')
|
829 |
+
|
830 |
+
#============Parameter setting===============
|
831 |
+
MODEL = 'Test' #['Train','Test','Test_other_method','Test_enantiomer','Test_excel']
|
832 |
+
test_mode='fixed' #fixed or random or enantiomer(extract enantimoers)
|
833 |
+
transfer_target='All_column' #trail name
|
834 |
+
Use_geometry_enhanced=True #default:True
|
835 |
+
Use_column_info=True #default: True
|
836 |
+
|
837 |
+
atom_id_names = [
|
838 |
+
"atomic_num", "chiral_tag", "degree", "explicit_valence",
|
839 |
+
"formal_charge", "hybridization", "implicit_valence",
|
840 |
+
"is_aromatic", "total_numHs",
|
841 |
+
]
|
842 |
+
bond_id_names = [
|
843 |
+
"bond_dir", "bond_type", "is_in_ring"]
|
844 |
+
|
845 |
+
if Use_geometry_enhanced==True:
|
846 |
+
bond_float_names = ["bond_length",'prop']
|
847 |
+
|
848 |
+
if Use_geometry_enhanced==False:
|
849 |
+
bond_float_names=['prop']
|
850 |
+
|
851 |
+
bond_angle_float_names = ['bond_angle', 'TPSA', 'RASA', 'RPSA', 'MDEC', 'MATS']
|
852 |
+
|
853 |
+
column_specify={'ADH':[1,5,0,0],'ODH':[1,5,0,1],'IC':[0,5,1,2],'IA':[0,5,1,3],'OJH':[1,5,0,4],
|
854 |
+
'ASH':[1,5,0,5],'IC3':[0,3,1,6],'IE':[0,5,1,7],'ID':[0,5,1,8],'OD3':[1,3,0,9],
|
855 |
+
'IB':[0,5,1,10],'AD':[1,10,0,11],'AD3':[1,3,0,12],'IF':[0,5,1,13],'OD':[1,10,0,14],
|
856 |
+
'AS':[1,10,0,15],'OJ3':[1,3,0,16],'IG':[0,5,1,17],'AZ':[1,10,0,18],'IAH':[0,5,1,19],
|
857 |
+
'OJ':[1,10,0,20],'ICH':[0,5,1,21],'OZ3':[1,3,0,22],'IF3':[0,3,1,23],'IAU':[0,1.6,1,24]}
|
858 |
+
column_smile=['O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4',
|
859 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@@H]1OC)NC4=CC(C)=CC(C)=C4',
|
860 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(Cl)=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC(Cl)=CC(Cl)=C3)=O)[C@@H]1OC)NC4=CC(Cl)=CC(Cl)=C4',
|
861 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4',
|
862 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(C2=CC=C(C)C=C2)=O)[C@@H](OC(C3=CC=C(C)C=C3)=O)[C@@H]1OC)C4=CC=C(C)C=C4',
|
863 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(N[C@@H](C)C2=CC=CC=C2)=O)[C@@H](OC(N[C@@H](C)C3=CC=CC=C3)=O)[C@H]1OC)N[C@@H](C)C4=CC=CC=C4',
|
864 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(Cl)=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC(Cl)=CC(Cl)=C3)=O)[C@@H]1OC)NC4=CC(Cl)=CC(Cl)=C4',
|
865 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(Cl)=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC(Cl)=CC(Cl)=C3)=O)[C@H]1OC)NC4=CC(Cl)=CC(Cl)=C4',
|
866 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC=CC(Cl)=C3)=O)[C@H]1OC)NC4=CC=CC(Cl)=C4',
|
867 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@@H]1OC)NC4=CC(C)=CC(C)=C4',
|
868 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@@H]1OC)NC4=CC(C)=CC(C)=C4',
|
869 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4',
|
870 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4',
|
871 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC=C(C)C(Cl)=C2)=O)[C@@H](OC(NC3=CC=C(C)C(Cl)=C3)=O)[C@H]1OC)NC4=CC=C(C)C(Cl)=C4',
|
872 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@@H]1OC)NC4=CC(C)=CC(C)=C4',
|
873 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(N[C@@H](C)C2=CC=CC=C2)=O)[C@@H](OC(N[C@@H](C)C3=CC=CC=C3)=O)[C@H]1OC)N[C@@H](C)C4=CC=CC=C4',
|
874 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(C2=CC=C(C)C=C2)=O)[C@@H](OC(C3=CC=C(C)C=C3)=O)[C@@H]1OC)C4=CC=C(C)C=C4',
|
875 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(Cl)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(Cl)=C4',
|
876 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC=C(C)C(Cl)=C2)=O)[C@@H](OC(NC3=CC=C(C)C(Cl)=C3)=O)[C@H]1OC)NC4=CC=C(C)C(Cl)=C4',
|
877 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4',
|
878 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(C2=CC=C(C)C=C2)=O)[C@@H](OC(C3=CC=C(C)C=C3)=O)[C@@H]1OC)C4=CC=C(C)C=C4',
|
879 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(Cl)=CC(Cl)=C2)=O)[C@@H](OC(NC3=CC(Cl)=CC(Cl)=C3)=O)[C@@H]1OC)NC4=CC(Cl)=CC(Cl)=C4',
|
880 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC=C(C)C(Cl)=C2)=O)[C@@H](OC(NC3=CC=C(C)C(Cl)=C3)=O)[C@@H]1OC)NC4=CC=C(C)C(Cl)=C4',
|
881 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC=C(C)C(Cl)=C2)=O)[C@@H](OC(NC3=CC=C(C)C(Cl)=C3)=O)[C@H]1OC)NC4=CC=C(C)C(Cl)=C4',
|
882 |
+
'O=C(OC[C@@H](O1)[C@@H](OC)[C@H](OC(NC2=CC(C)=CC(C)=C2)=O)[C@@H](OC(NC3=CC(C)=CC(C)=C3)=O)[C@H]1OC)NC4=CC(C)=CC(C)=C4']
|
883 |
+
column_name=['ADH','ODH','IC','IA','OJH','ASH','IC3','IE','ID','OD3', 'IB','AD','AD3',
|
884 |
+
'IF','OD','AS','OJ3','IG','AZ','IAH','OJ','ICH','OZ3','IF3','IAU']
|
885 |
+
full_atom_feature_dims = get_atom_feature_dims(atom_id_names)
|
886 |
+
full_bond_feature_dims = get_bond_feature_dims(bond_id_names)
|
887 |
+
|
888 |
+
|
889 |
+
if Use_column_info==True:
|
890 |
+
bond_id_names.extend(['coated', 'immobilized'])
|
891 |
+
bond_float_names.extend(['diameter'])
|
892 |
+
if Use_geometry_enhanced==True:
|
893 |
+
bond_angle_float_names.extend(['column_TPSA', 'column_TPSA', 'column_TPSA', 'column_MDEC', 'column_MATS'])
|
894 |
+
else:
|
895 |
+
bond_float_names.extend(['column_TPSA', 'column_TPSA', 'column_TPSA', 'column_MDEC', 'column_MATS'])
|
896 |
+
full_bond_feature_dims.extend([2,2])
|
897 |
+
|
898 |
+
calc = Calculator(descriptors, ignore_3D=False)
|
899 |
+
|
900 |
+
|
901 |
+
class AtomEncoder(torch.nn.Module):
|
902 |
+
|
903 |
+
def __init__(self, emb_dim):
|
904 |
+
super(AtomEncoder, self).__init__()
|
905 |
+
|
906 |
+
self.atom_embedding_list = torch.nn.ModuleList()
|
907 |
+
|
908 |
+
for i, dim in enumerate(full_atom_feature_dims):
|
909 |
+
emb = torch.nn.Embedding(dim + 5, emb_dim) # 不同维度的属性用不同的Embedding方法
|
910 |
+
torch.nn.init.xavier_uniform_(emb.weight.data)
|
911 |
+
self.atom_embedding_list.append(emb)
|
912 |
+
|
913 |
+
def forward(self, x):
|
914 |
+
x_embedding = 0
|
915 |
+
for i in range(x.shape[1]):
|
916 |
+
x_embedding += self.atom_embedding_list[i](x[:, i])
|
917 |
+
|
918 |
+
return x_embedding
|
919 |
+
|
920 |
+
class BondEncoder(torch.nn.Module):
|
921 |
+
|
922 |
+
def __init__(self, emb_dim):
|
923 |
+
super(BondEncoder, self).__init__()
|
924 |
+
|
925 |
+
self.bond_embedding_list = torch.nn.ModuleList()
|
926 |
+
|
927 |
+
for i, dim in enumerate(full_bond_feature_dims):
|
928 |
+
emb = torch.nn.Embedding(dim + 5, emb_dim)
|
929 |
+
torch.nn.init.xavier_uniform_(emb.weight.data)
|
930 |
+
self.bond_embedding_list.append(emb)
|
931 |
+
|
932 |
+
def forward(self, edge_attr):
|
933 |
+
bond_embedding = 0
|
934 |
+
for i in range(edge_attr.shape[1]):
|
935 |
+
bond_embedding += self.bond_embedding_list[i](edge_attr[:, i])
|
936 |
+
|
937 |
+
return bond_embedding
|
938 |
+
|
939 |
+
class RBF(torch.nn.Module):
|
940 |
+
"""
|
941 |
+
Radial Basis Function
|
942 |
+
"""
|
943 |
+
|
944 |
+
def __init__(self, centers, gamma, dtype='float32'):
|
945 |
+
super(RBF, self).__init__()
|
946 |
+
self.centers = centers.reshape([1, -1])
|
947 |
+
self.gamma = gamma
|
948 |
+
|
949 |
+
def forward(self, x):
|
950 |
+
"""
|
951 |
+
Args:
|
952 |
+
x(tensor): (-1, 1).
|
953 |
+
Returns:
|
954 |
+
y(tensor): (-1, n_centers)
|
955 |
+
"""
|
956 |
+
x = x.reshape([-1, 1])
|
957 |
+
return torch.exp(-self.gamma * torch.square(x - self.centers))
|
958 |
+
|
959 |
+
class BondFloatRBF(torch.nn.Module):
|
960 |
+
"""
|
961 |
+
Bond Float Encoder using Radial Basis Functions
|
962 |
+
"""
|
963 |
+
|
964 |
+
def __init__(self, bond_float_names, embed_dim, rbf_params=None):
|
965 |
+
super(BondFloatRBF, self).__init__()
|
966 |
+
self.bond_float_names = bond_float_names
|
967 |
+
|
968 |
+
if rbf_params is None:
|
969 |
+
self.rbf_params = {
|
970 |
+
'bond_length': (nn.Parameter(torch.arange(0, 2, 0.1)), nn.Parameter(torch.Tensor([10.0]))),
|
971 |
+
# (centers, gamma)
|
972 |
+
'prop': (nn.Parameter(torch.arange(0, 1, 0.05)), nn.Parameter(torch.Tensor([1.0]))),
|
973 |
+
'diameter': (nn.Parameter(torch.arange(3, 12, 0.3)), nn.Parameter(torch.Tensor([1.0]))),
|
974 |
+
##=========Only for pure GNN===============
|
975 |
+
'column_TPSA': (nn.Parameter(torch.arange(0, 1, 0.05).to(torch.float32)), nn.Parameter(torch.Tensor([1.0]))),
|
976 |
+
'column_RASA': (nn.Parameter(torch.arange(0, 1, 0.05)), nn.Parameter(torch.Tensor([1.0]))),
|
977 |
+
'column_RPSA': (nn.Parameter(torch.arange(0, 1, 0.05)), nn.Parameter(torch.Tensor([1.0]))),
|
978 |
+
'column_MDEC': (nn.Parameter(torch.arange(0, 10, 0.5)), nn.Parameter(torch.Tensor([2.0]))),
|
979 |
+
'column_MATS': (nn.Parameter(torch.arange(0, 1, 0.05)), nn.Parameter(torch.Tensor([1.0]))),
|
980 |
+
}
|
981 |
+
else:
|
982 |
+
self.rbf_params = rbf_params
|
983 |
+
|
984 |
+
self.linear_list = torch.nn.ModuleList()
|
985 |
+
self.rbf_list = torch.nn.ModuleList()
|
986 |
+
for name in self.bond_float_names:
|
987 |
+
centers, gamma = self.rbf_params[name]
|
988 |
+
rbf = RBF(centers.to(device), gamma.to(device))
|
989 |
+
self.rbf_list.append(rbf)
|
990 |
+
linear = torch.nn.Linear(len(centers), embed_dim).cuda()
|
991 |
+
self.linear_list.append(linear)
|
992 |
+
|
993 |
+
def forward(self, bond_float_features):
|
994 |
+
"""
|
995 |
+
Args:
|
996 |
+
bond_float_features(dict of tensor): bond float features.
|
997 |
+
"""
|
998 |
+
out_embed = 0
|
999 |
+
for i, name in enumerate(self.bond_float_names):
|
1000 |
+
x = bond_float_features[:, i].reshape(-1, 1)
|
1001 |
+
rbf_x = self.rbf_list[i](x)
|
1002 |
+
out_embed += self.linear_list[i](rbf_x)
|
1003 |
+
return out_embed
|
1004 |
+
|
1005 |
+
class BondAngleFloatRBF(torch.nn.Module):
|
1006 |
+
"""
|
1007 |
+
Bond Angle Float Encoder using Radial Basis Functions
|
1008 |
+
"""
|
1009 |
+
|
1010 |
+
def __init__(self, bond_angle_float_names, embed_dim, rbf_params=None):
|
1011 |
+
super(BondAngleFloatRBF, self).__init__()
|
1012 |
+
self.bond_angle_float_names = bond_angle_float_names
|
1013 |
+
|
1014 |
+
if rbf_params is None:
|
1015 |
+
self.rbf_params = {
|
1016 |
+
'bond_angle': (nn.Parameter(torch.arange(0, torch.pi, 0.1)), nn.Parameter(torch.Tensor([10.0]))),
|
1017 |
+
}
|
1018 |
+
else:
|
1019 |
+
self.rbf_params = rbf_params
|
1020 |
+
|
1021 |
+
self.linear_list = torch.nn.ModuleList()
|
1022 |
+
self.rbf_list = torch.nn.ModuleList()
|
1023 |
+
for name in self.bond_angle_float_names:
|
1024 |
+
if name == 'bond_angle':
|
1025 |
+
centers, gamma = self.rbf_params[name]
|
1026 |
+
rbf = RBF(centers.to(device), gamma.to(device))
|
1027 |
+
self.rbf_list.append(rbf)
|
1028 |
+
linear = nn.Linear(len(centers), embed_dim)
|
1029 |
+
self.linear_list.append(linear)
|
1030 |
+
else:
|
1031 |
+
linear = nn.Linear(len(self.bond_angle_float_names) - 1, embed_dim)
|
1032 |
+
self.linear_list.append(linear)
|
1033 |
+
break
|
1034 |
+
|
1035 |
+
def forward(self, bond_angle_float_features):
|
1036 |
+
"""
|
1037 |
+
Args:
|
1038 |
+
bond_angle_float_features(dict of tensor): bond angle float features.
|
1039 |
+
"""
|
1040 |
+
out_embed = 0
|
1041 |
+
for i, name in enumerate(self.bond_angle_float_names):
|
1042 |
+
if name == 'bond_angle':
|
1043 |
+
x = bond_angle_float_features[:, i].reshape(-1, 1)
|
1044 |
+
rbf_x = self.rbf_list[i](x)
|
1045 |
+
out_embed += self.linear_list[i](rbf_x)
|
1046 |
+
else:
|
1047 |
+
x = bond_angle_float_features[:, 1:]
|
1048 |
+
out_embed += self.linear_list[i](x)
|
1049 |
+
break
|
1050 |
+
return out_embed
|
1051 |
+
|
1052 |
+
class GINConv(MessagePassing):
|
1053 |
+
def __init__(self, emb_dim):
|
1054 |
+
'''
|
1055 |
+
emb_dim (int): node embedding dimensionality
|
1056 |
+
'''
|
1057 |
+
|
1058 |
+
super(GINConv, self).__init__(aggr="add")
|
1059 |
+
|
1060 |
+
self.mlp = nn.Sequential(nn.Linear(emb_dim, emb_dim), nn.BatchNorm1d(emb_dim), nn.ReLU(),
|
1061 |
+
nn.Linear(emb_dim, emb_dim))
|
1062 |
+
self.eps = nn.Parameter(torch.Tensor([0]))
|
1063 |
+
|
1064 |
+
def forward(self, x, edge_index, edge_attr):
|
1065 |
+
edge_embedding = edge_attr
|
1066 |
+
out = self.mlp((1 + self.eps) * x + self.propagate(edge_index, x=x, edge_attr=edge_embedding))
|
1067 |
+
return out
|
1068 |
+
|
1069 |
+
def message(self, x_j, edge_attr):
|
1070 |
+
return F.relu(x_j + edge_attr)
|
1071 |
+
|
1072 |
+
def update(self, aggr_out):
|
1073 |
+
return aggr_out
|
1074 |
+
|
1075 |
+
# GNN to generate node embedding
|
1076 |
+
class GINNodeEmbedding(torch.nn.Module):
|
1077 |
+
"""
|
1078 |
+
Output:
|
1079 |
+
node representations
|
1080 |
+
"""
|
1081 |
+
|
1082 |
+
def __init__(self, num_layers, emb_dim, drop_ratio=0.5, JK="last", residual=False):
|
1083 |
+
"""GIN Node Embedding Module
|
1084 |
+
采用多层GINConv实现图上结点的嵌入。
|
1085 |
+
"""
|
1086 |
+
|
1087 |
+
super(GINNodeEmbedding, self).__init__()
|
1088 |
+
self.num_layers = num_layers
|
1089 |
+
self.drop_ratio = drop_ratio
|
1090 |
+
self.JK = JK
|
1091 |
+
# add residual connection or not
|
1092 |
+
self.residual = residual
|
1093 |
+
|
1094 |
+
if self.num_layers < 2:
|
1095 |
+
raise ValueError("Number of GNN layers must be greater than 1.")
|
1096 |
+
|
1097 |
+
self.atom_encoder = AtomEncoder(emb_dim)
|
1098 |
+
self.bond_encoder=BondEncoder(emb_dim)
|
1099 |
+
self.bond_float_encoder=BondFloatRBF(bond_float_names,emb_dim)
|
1100 |
+
self.bond_angle_encoder=BondAngleFloatRBF(bond_angle_float_names,emb_dim)
|
1101 |
+
|
1102 |
+
# List of GNNs
|
1103 |
+
self.convs = torch.nn.ModuleList()
|
1104 |
+
self.convs_bond_angle=torch.nn.ModuleList()
|
1105 |
+
self.convs_bond_float=torch.nn.ModuleList()
|
1106 |
+
self.convs_bond_embeding=torch.nn.ModuleList()
|
1107 |
+
self.convs_angle_float=torch.nn.ModuleList()
|
1108 |
+
self.batch_norms = torch.nn.ModuleList()
|
1109 |
+
self.batch_norms_ba = torch.nn.ModuleList()
|
1110 |
+
for layer in range(num_layers):
|
1111 |
+
self.convs.append(GINConv(emb_dim))
|
1112 |
+
self.convs_bond_angle.append(GINConv(emb_dim))
|
1113 |
+
self.convs_bond_embeding.append(BondEncoder(emb_dim))
|
1114 |
+
self.convs_bond_float.append(BondFloatRBF(bond_float_names,emb_dim))
|
1115 |
+
self.convs_angle_float.append(BondAngleFloatRBF(bond_angle_float_names,emb_dim))
|
1116 |
+
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
|
1117 |
+
self.batch_norms_ba.append(torch.nn.BatchNorm1d(emb_dim))
|
1118 |
+
|
1119 |
+
def forward(self, batched_atom_bond,batched_bond_angle):
|
1120 |
+
x, edge_index, edge_attr = batched_atom_bond.x, batched_atom_bond.edge_index, batched_atom_bond.edge_attr
|
1121 |
+
edge_index_ba,edge_attr_ba= batched_bond_angle.edge_index, batched_bond_angle.edge_attr
|
1122 |
+
# computing input node embedding
|
1123 |
+
h_list = [self.atom_encoder(x)] # 先将类别型原子属性转化为原子嵌入
|
1124 |
+
|
1125 |
+
if Use_geometry_enhanced==True:
|
1126 |
+
h_list_ba=[self.bond_float_encoder(edge_attr[:,len(bond_id_names):edge_attr.shape[1]+1].to(torch.float32))+self.bond_encoder(edge_attr[:,0:len(bond_id_names)].to(torch.int64))]
|
1127 |
+
for layer in range(self.num_layers):
|
1128 |
+
h = self.convs[layer](h_list[layer], edge_index, h_list_ba[layer])
|
1129 |
+
cur_h_ba=self.convs_bond_embeding[layer](edge_attr[:,0:len(bond_id_names)].to(torch.int64))+self.convs_bond_float[layer](edge_attr[:,len(bond_id_names):edge_attr.shape[1]+1].to(torch.float32))
|
1130 |
+
cur_angle_hidden=self.convs_angle_float[layer](edge_attr_ba)
|
1131 |
+
h_ba=self.convs_bond_angle[layer](cur_h_ba, edge_index_ba, cur_angle_hidden)
|
1132 |
+
|
1133 |
+
if layer == self.num_layers - 1:
|
1134 |
+
# remove relu for the last layer
|
1135 |
+
h = F.dropout(h, self.drop_ratio, training=self.training)
|
1136 |
+
h_ba = F.dropout(h_ba, self.drop_ratio, training=self.training)
|
1137 |
+
else:
|
1138 |
+
h = F.dropout(F.relu(h), self.drop_ratio, training=self.training)
|
1139 |
+
h_ba = F.dropout(F.relu(h_ba), self.drop_ratio, training=self.training)
|
1140 |
+
if self.residual:
|
1141 |
+
h += h_list[layer]
|
1142 |
+
h_ba+=h_list_ba[layer]
|
1143 |
+
h_list.append(h)
|
1144 |
+
h_list_ba.append(h_ba)
|
1145 |
+
|
1146 |
+
|
1147 |
+
# Different implementations of Jk-concat
|
1148 |
+
if self.JK == "last":
|
1149 |
+
node_representation = h_list[-1]
|
1150 |
+
edge_representation = h_list_ba[-1]
|
1151 |
+
elif self.JK == "sum":
|
1152 |
+
node_representation = 0
|
1153 |
+
edge_representation = 0
|
1154 |
+
for layer in range(self.num_layers + 1):
|
1155 |
+
node_representation += h_list[layer]
|
1156 |
+
edge_representation += h_list_ba[layer]
|
1157 |
+
|
1158 |
+
return node_representation,edge_representation
|
1159 |
+
if Use_geometry_enhanced==False:
|
1160 |
+
for layer in range(self.num_layers):
|
1161 |
+
h = self.convs[layer](h_list[layer], edge_index,
|
1162 |
+
self.convs_bond_embeding[layer](edge_attr[:, 0:len(bond_id_names)].to(torch.int64)) +
|
1163 |
+
self.convs_bond_float[layer](
|
1164 |
+
edge_attr[:, len(bond_id_names):edge_attr.shape[1] + 1].to(torch.float32)))
|
1165 |
+
h = self.batch_norms[layer](h)
|
1166 |
+
if layer == self.num_layers - 1:
|
1167 |
+
# remove relu for the last layer
|
1168 |
+
h = F.dropout(h, self.drop_ratio, training=self.training)
|
1169 |
+
else:
|
1170 |
+
h = F.dropout(F.relu(h), self.drop_ratio, training=self.training)
|
1171 |
+
|
1172 |
+
if self.residual:
|
1173 |
+
h += h_list[layer]
|
1174 |
+
|
1175 |
+
h_list.append(h)
|
1176 |
+
|
1177 |
+
# Different implementations of Jk-concat
|
1178 |
+
if self.JK == "last":
|
1179 |
+
node_representation = h_list[-1]
|
1180 |
+
elif self.JK == "sum":
|
1181 |
+
node_representation = 0
|
1182 |
+
for layer in range(self.num_layers + 1):
|
1183 |
+
node_representation += h_list[layer]
|
1184 |
+
|
1185 |
+
return node_representation
|
1186 |
+
|
1187 |
+
class GINGraphPooling(nn.Module):
|
1188 |
+
|
1189 |
+
def __init__(self, num_tasks=1, num_layers=5, emb_dim=300, residual=False, drop_ratio=0, JK="last", graph_pooling="attention",
|
1190 |
+
descriptor_dim=1781):
|
1191 |
+
"""GIN Graph Pooling Module
|
1192 |
+
|
1193 |
+
此模块首先采用GINNodeEmbedding模块对图上每一个节点做嵌入,然后对节点嵌入做池化得到图的嵌入,最后用一层线性变换得到图的最终的表示(graph representation)。
|
1194 |
+
|
1195 |
+
Args:
|
1196 |
+
num_tasks (int, optional): number of labels to be predicted. Defaults to 1 (控制了图表示的维度,dimension of graph representation).
|
1197 |
+
num_layers (int, optional): number of GINConv layers. Defaults to 5.
|
1198 |
+
emb_dim (int, optional): dimension of node embedding. Defaults to 300.
|
1199 |
+
residual (bool, optional): adding residual connection or not. Defaults to False.
|
1200 |
+
drop_ratio (float, optional): dropout rate. Defaults to 0.
|
1201 |
+
JK (str, optional): 可选的值为"last"和"sum"。选"last",只取最后一层的结点的嵌入,选"sum"对各层的结点的嵌入求和。Defaults to "last".
|
1202 |
+
graph_pooling (str, optional): pooling method of node embedding. 可选的值为"sum","mean","max","attention"和"set2set"。 Defaults to "sum".
|
1203 |
+
|
1204 |
+
Out:
|
1205 |
+
graph representation
|
1206 |
+
"""
|
1207 |
+
super(GINGraphPooling, self).__init__()
|
1208 |
+
|
1209 |
+
self.num_layers = num_layers
|
1210 |
+
self.drop_ratio = drop_ratio
|
1211 |
+
self.JK = JK
|
1212 |
+
self.emb_dim = emb_dim
|
1213 |
+
self.num_tasks = num_tasks
|
1214 |
+
self.descriptor_dim=descriptor_dim
|
1215 |
+
if self.num_layers < 2:
|
1216 |
+
raise ValueError("Number of GNN layers must be greater than 1.")
|
1217 |
+
|
1218 |
+
self.gnn_node = GINNodeEmbedding(num_layers, emb_dim, JK=JK, drop_ratio=drop_ratio, residual=residual)
|
1219 |
+
|
1220 |
+
# Pooling function to generate whole-graph embeddings
|
1221 |
+
if graph_pooling == "sum":
|
1222 |
+
self.pool = global_add_pool
|
1223 |
+
|
1224 |
+
elif graph_pooling == "mean":
|
1225 |
+
self.pool = global_mean_pool
|
1226 |
+
|
1227 |
+
elif graph_pooling == "max":
|
1228 |
+
self.pool = global_max_pool
|
1229 |
+
|
1230 |
+
elif graph_pooling == "attention":
|
1231 |
+
self.pool = GlobalAttention(gate_nn=nn.Sequential(
|
1232 |
+
nn.Linear(emb_dim, emb_dim), nn.BatchNorm1d(emb_dim), nn.ReLU(), nn.Linear(emb_dim, 1)))
|
1233 |
+
|
1234 |
+
|
1235 |
+
elif graph_pooling == "set2set":
|
1236 |
+
self.pool = Set2Set(emb_dim, processing_steps=2)
|
1237 |
+
else:
|
1238 |
+
raise ValueError("Invalid graph pooling type.")
|
1239 |
+
|
1240 |
+
if graph_pooling == "set2set":
|
1241 |
+
self.graph_pred_linear = nn.Linear(self.emb_dim, self.num_tasks)
|
1242 |
+
else:
|
1243 |
+
self.graph_pred_linear = nn.Linear(self.emb_dim, self.num_tasks)
|
1244 |
+
|
1245 |
+
self.NN_descriptor = nn.Sequential(nn.Linear(self.descriptor_dim, self.emb_dim),
|
1246 |
+
nn.Sigmoid(),
|
1247 |
+
nn.Linear(self.emb_dim, self.emb_dim))
|
1248 |
+
|
1249 |
+
self.sigmoid = nn.Sigmoid()
|
1250 |
+
|
1251 |
+
def forward(self, batched_atom_bond,batched_bond_angle):
|
1252 |
+
if Use_geometry_enhanced==True:
|
1253 |
+
h_node,h_node_ba= self.gnn_node(batched_atom_bond,batched_bond_angle)
|
1254 |
+
else:
|
1255 |
+
h_node= self.gnn_node(batched_atom_bond, batched_bond_angle)
|
1256 |
+
h_graph = self.pool(h_node, batched_atom_bond.batch)
|
1257 |
+
output = self.graph_pred_linear(h_graph)
|
1258 |
+
if self.training:
|
1259 |
+
return output,h_graph
|
1260 |
+
else:
|
1261 |
+
# At inference time, relu is applied to output to ensure positivity
|
1262 |
+
return torch.clamp(output, min=0, max=1e8),h_graph
|
1263 |
+
|
1264 |
+
def mord(mol, nBits=1826, errors_as_zeros=True):
|
1265 |
+
try:
|
1266 |
+
result = calc(mol)
|
1267 |
+
desc_list = [r if not is_missing(r) else 0 for r in result]
|
1268 |
+
np_arr = np.array(desc_list)
|
1269 |
+
return np_arr
|
1270 |
+
except:
|
1271 |
+
return np.NaN if not errors_as_zeros else np.zeros((nBits,), dtype=np.float32)
|
1272 |
+
|
1273 |
+
def load_3D_mol():
|
1274 |
+
dir = 'mol_save/'
|
1275 |
+
for root, dirs, files in os.walk(dir):
|
1276 |
+
file_names = files
|
1277 |
+
file_names.sort(key=lambda x: int(x[x.find('_') + 5:x.find(".")])) # 按照前面的数字字符排序
|
1278 |
+
mol_save = []
|
1279 |
+
for file_name in file_names:
|
1280 |
+
mol_save.append(Chem.MolFromMolFile(dir + file_name))
|
1281 |
+
return mol_save
|
1282 |
+
|
1283 |
+
def parse_args():
|
1284 |
+
parser = argparse.ArgumentParser(description='Graph data miming with GNN')
|
1285 |
+
parser.add_argument('--task_name', type=str, default='GINGraphPooling',
|
1286 |
+
help='task name')
|
1287 |
+
parser.add_argument('--device', type=int, default=0,
|
1288 |
+
help='which gpu to use if any (default: 0)')
|
1289 |
+
parser.add_argument('--num_layers', type=int, default=5,
|
1290 |
+
help='number of GNN message passing layers (default: 5)')
|
1291 |
+
parser.add_argument('--graph_pooling', type=str, default='sum',
|
1292 |
+
help='graph pooling strategy mean or sum (default: sum)')
|
1293 |
+
parser.add_argument('--emb_dim', type=int, default=128,
|
1294 |
+
help='dimensionality of hidden units in GNNs (default: 256)')
|
1295 |
+
parser.add_argument('--drop_ratio', type=float, default=0.,
|
1296 |
+
help='dropout ratio (default: 0.)')
|
1297 |
+
parser.add_argument('--save_test', action='store_true')
|
1298 |
+
parser.add_argument('--batch_size', type=int, default=2048,
|
1299 |
+
help='input batch size for training (default: 512)')
|
1300 |
+
parser.add_argument('--epochs', type=int, default=1000,
|
1301 |
+
help='number of epochs to train (default: 100)')
|
1302 |
+
parser.add_argument('--weight_decay', type=float, default=0.00001,
|
1303 |
+
help='weight decay')
|
1304 |
+
parser.add_argument('--early_stop', type=int, default=10,
|
1305 |
+
help='early stop (default: 10)')
|
1306 |
+
parser.add_argument('--num_workers', type=int, default=0,
|
1307 |
+
help='number of workers (default: 0)')
|
1308 |
+
parser.add_argument('--dataset_root', type=str, default="dataset",
|
1309 |
+
help='dataset root')
|
1310 |
+
args = parser.parse_args()
|
1311 |
+
|
1312 |
+
return args
|
1313 |
+
|
1314 |
+
def calc_dragon_type_desc(mol):
|
1315 |
+
compound_mol = mol
|
1316 |
+
compound_MolWt = Descriptors.ExactMolWt(compound_mol)
|
1317 |
+
compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol)
|
1318 |
+
compound_nRotB = Descriptors.NumRotatableBonds(compound_mol) # Number of rotable bonds
|
1319 |
+
compound_HBD = Descriptors.NumHDonors(compound_mol) # Number of H bond donors
|
1320 |
+
compound_HBA = Descriptors.NumHAcceptors(compound_mol) # Number of H bond acceptors
|
1321 |
+
compound_LogP = Descriptors.MolLogP(compound_mol) # LogP
|
1322 |
+
return rdMolDescriptors.CalcAUTOCORR3D(mol) + rdMolDescriptors.CalcMORSE(mol) + \
|
1323 |
+
rdMolDescriptors.CalcRDF(mol) + rdMolDescriptors.CalcWHIM(mol) + \
|
1324 |
+
[compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP]
|
1325 |
+
|
1326 |
+
|
1327 |
+
def eval(model, device, loader_atom_bond,loader_bond_angle):
|
1328 |
+
model.eval()
|
1329 |
+
y_true = []
|
1330 |
+
y_pred = []
|
1331 |
+
y_pred_10=[]
|
1332 |
+
y_pred_90=[]
|
1333 |
+
|
1334 |
+
with torch.no_grad():
|
1335 |
+
for _, batch in enumerate(zip(loader_atom_bond,loader_bond_angle)):
|
1336 |
+
batch_atom_bond = batch[0]
|
1337 |
+
batch_bond_angle = batch[1]
|
1338 |
+
batch_atom_bond = batch_atom_bond.to(device)
|
1339 |
+
batch_bond_angle = batch_bond_angle.to(device)
|
1340 |
+
pred = model(batch_atom_bond,batch_bond_angle)[0]
|
1341 |
+
|
1342 |
+
y_true.append(batch_atom_bond.y.detach().cpu().reshape(-1))
|
1343 |
+
y_pred.append(pred[:,1].detach().cpu())
|
1344 |
+
y_pred_10.append(pred[:,0].detach().cpu())
|
1345 |
+
y_pred_90.append(pred[:,2].detach().cpu())
|
1346 |
+
y_true = torch.cat(y_true, dim=0)
|
1347 |
+
y_pred = torch.cat(y_pred, dim=0)
|
1348 |
+
y_pred_10 = torch.cat(y_pred_10, dim=0)
|
1349 |
+
y_pred_90 = torch.cat(y_pred_90, dim=0)
|
1350 |
+
# plt.plot(y_pred.cpu().data.numpy(),c='blue')
|
1351 |
+
# plt.plot(y_pred_10.cpu().data.numpy(),c='yellow')
|
1352 |
+
# plt.plot(y_pred_90.cpu().data.numpy(),c='black')
|
1353 |
+
# plt.plot(y_true.cpu().data.numpy(),c='red')
|
1354 |
+
#plt.show()
|
1355 |
+
input_dict = {"y_true": y_true, "y_pred": y_pred}
|
1356 |
+
return torch.mean((y_true - y_pred) ** 2).data.numpy()
|
1357 |
+
|
1358 |
+
|
1359 |
+
def cal_prob(prediction):
|
1360 |
+
'''
|
1361 |
+
calculate the separation probability Sp
|
1362 |
+
'''
|
1363 |
+
#input prediction=[pred_1,pred_2]
|
1364 |
+
#output: Sp
|
1365 |
+
a=prediction[0][0]
|
1366 |
+
b=prediction[1][0]
|
1367 |
+
if a[2]<b[0]:
|
1368 |
+
return 1
|
1369 |
+
elif a[0]>b[2]:
|
1370 |
+
return 1
|
1371 |
+
else:
|
1372 |
+
length=min(a[2],b[2])-max(a[0],b[0])
|
1373 |
+
all=max(a[2],b[2])-min(a[0],b[0])
|
1374 |
+
return 1-length/(all)
|
1375 |
+
|
1376 |
+
|
1377 |
+
|
1378 |
+
args = parse_args()
|
1379 |
+
nn_params = {
|
1380 |
+
'num_tasks': 3,
|
1381 |
+
'num_layers': args.num_layers,
|
1382 |
+
'emb_dim': args.emb_dim,
|
1383 |
+
'drop_ratio': args.drop_ratio,
|
1384 |
+
'graph_pooling': args.graph_pooling,
|
1385 |
+
'descriptor_dim': 1827
|
1386 |
+
}
|
1387 |
+
device = args.device
|
1388 |
+
model = GINGraphPooling(**nn_params).to(device)
|
1389 |
+
|
1390 |
+
|
1391 |
+
'''
|
1392 |
+
Given two compounds and predict the RT in different condition
|
1393 |
+
'''
|
1394 |
+
|
1395 |
+
|
1396 |
+
def predict_separate(smile_1, smile_2, input_eluent, input_speed, input_column):
|
1397 |
+
speed = []
|
1398 |
+
eluent = []
|
1399 |
+
smiles=[smile_1,smile_2]
|
1400 |
+
for i in range(2):
|
1401 |
+
speed.append(input_speed)
|
1402 |
+
eluent.append(input_eluent)
|
1403 |
+
|
1404 |
+
column_descriptor = np.load('column_descriptor.npy', allow_pickle=True)
|
1405 |
+
predict_column=input_column
|
1406 |
+
col_specify = column_specify[predict_column]
|
1407 |
+
col_des = np.array(column_descriptor[col_specify[3]])
|
1408 |
+
mols = []
|
1409 |
+
y_pred = []
|
1410 |
+
all_descriptor = []
|
1411 |
+
dataset = []
|
1412 |
+
for smile in smiles:
|
1413 |
+
mol = Chem.MolFromSmiles(smile)
|
1414 |
+
mols.append(mol)
|
1415 |
+
for smile in smiles:
|
1416 |
+
mol = obtain_3D_mol(smile, 'conform')
|
1417 |
+
mol = Chem.MolFromMolFile(f"conform.mol")
|
1418 |
+
all_descriptor.append(mord(mol))
|
1419 |
+
dataset.append(mol_to_geognn_graph_data_MMFF3d(mol))
|
1420 |
+
|
1421 |
+
for i in range(0, len(dataset)):
|
1422 |
+
data = dataset[i]
|
1423 |
+
atom_feature = []
|
1424 |
+
bond_feature = []
|
1425 |
+
for name in atom_id_names:
|
1426 |
+
atom_feature.append(data[name])
|
1427 |
+
for name in bond_id_names[0:3]:
|
1428 |
+
bond_feature.append(data[name])
|
1429 |
+
atom_feature = torch.from_numpy(np.array(atom_feature).T).to(torch.int64)
|
1430 |
+
bond_feature = torch.from_numpy(np.array(bond_feature).T).to(torch.int64)
|
1431 |
+
bond_float_feature = torch.from_numpy(data['bond_length'].astype(np.float32))
|
1432 |
+
bond_angle_feature = torch.from_numpy(data['bond_angle'].astype(np.float32))
|
1433 |
+
y = torch.Tensor([float(speed[i])])
|
1434 |
+
edge_index = torch.from_numpy(data['edges'].T).to(torch.int64)
|
1435 |
+
bond_index = torch.from_numpy(data['BondAngleGraph_edges'].T).to(torch.int64)
|
1436 |
+
|
1437 |
+
prop = torch.ones([bond_feature.shape[0]]) * eluent[i]
|
1438 |
+
coated = torch.ones([bond_feature.shape[0]]) * col_specify[0]
|
1439 |
+
diameter = torch.ones([bond_feature.shape[0]]) * col_specify[1]
|
1440 |
+
immobilized = torch.ones([bond_feature.shape[0]]) * col_specify[2]
|
1441 |
+
|
1442 |
+
TPSA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][820] / 100
|
1443 |
+
RASA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][821]
|
1444 |
+
RPSA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][822]
|
1445 |
+
MDEC = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][1568]
|
1446 |
+
MATS = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][457]
|
1447 |
+
|
1448 |
+
col_TPSA = torch.ones([bond_angle_feature.shape[0]]) * col_des[820] / 100
|
1449 |
+
col_RASA = torch.ones([bond_angle_feature.shape[0]]) * col_des[821]
|
1450 |
+
col_RPSA = torch.ones([bond_angle_feature.shape[0]]) * col_des[822]
|
1451 |
+
col_MDEC = torch.ones([bond_angle_feature.shape[0]]) * col_des[1568]
|
1452 |
+
col_MATS = torch.ones([bond_angle_feature.shape[0]]) * col_des[457]
|
1453 |
+
|
1454 |
+
bond_feature = torch.cat([bond_feature, coated.reshape(-1, 1)], dim=1)
|
1455 |
+
bond_feature = torch.cat([bond_feature, immobilized.reshape(-1, 1)], dim=1)
|
1456 |
+
bond_feature = torch.cat([bond_feature, bond_float_feature.reshape(-1, 1)], dim=1)
|
1457 |
+
bond_feature = torch.cat([bond_feature, prop.reshape(-1, 1)], dim=1)
|
1458 |
+
bond_feature = torch.cat([bond_feature, diameter.reshape(-1, 1)], dim=1)
|
1459 |
+
|
1460 |
+
bond_angle_feature = torch.cat([bond_angle_feature.reshape(-1, 1), TPSA.reshape(-1, 1)], dim=1)
|
1461 |
+
bond_angle_feature = torch.cat([bond_angle_feature, RASA.reshape(-1, 1)], dim=1)
|
1462 |
+
bond_angle_feature = torch.cat([bond_angle_feature, RPSA.reshape(-1, 1)], dim=1)
|
1463 |
+
bond_angle_feature = torch.cat([bond_angle_feature, MDEC.reshape(-1, 1)], dim=1)
|
1464 |
+
bond_angle_feature = torch.cat([bond_angle_feature, MATS.reshape(-1, 1)], dim=1)
|
1465 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_TPSA.reshape(-1, 1)], dim=1)
|
1466 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_RASA.reshape(-1, 1)], dim=1)
|
1467 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_RPSA.reshape(-1, 1)], dim=1)
|
1468 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_MDEC.reshape(-1, 1)], dim=1)
|
1469 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_MATS.reshape(-1, 1)], dim=1)
|
1470 |
+
|
1471 |
+
data_atom_bond = Data(atom_feature, edge_index, bond_feature, y)
|
1472 |
+
data_bond_angle = Data(edge_index=bond_index, edge_attr=bond_angle_feature)
|
1473 |
+
model.load_state_dict(
|
1474 |
+
torch.load(f'GeoGNN_model.pth',map_location=torch.device('cpu')))
|
1475 |
+
model.eval()
|
1476 |
+
|
1477 |
+
pred, h_graph = model(data_atom_bond.to(device), data_bond_angle.to(device))
|
1478 |
+
|
1479 |
+
y_pred.append(pred.detach().cpu().data.numpy() / speed[i])
|
1480 |
+
if input_speed==0:
|
1481 |
+
out_put='Speed cannot be 0!'
|
1482 |
+
else:
|
1483 |
+
Sp=cal_prob(y_pred)
|
1484 |
+
output_1=f'For smile_1,\n the predicted value is: {str(np.round(y_pred[0][0][1],3))}\n'
|
1485 |
+
output_2 = f'For smile_2,\n the predicted value is: {str(np.round(y_pred[1][0][1],3))}\n'
|
1486 |
+
output_3=f'The separation probability is: {str(np.round(Sp,3))}'
|
1487 |
+
out_put=output_1+output_2+output_3
|
1488 |
+
return out_put
|
1489 |
+
|
1490 |
+
|
1491 |
+
def column_recommendation(smile_1, smile_2, input_eluent, input_speed):
|
1492 |
+
speed = []
|
1493 |
+
eluent = []
|
1494 |
+
Prediction = []
|
1495 |
+
Sp = []
|
1496 |
+
smiles = [smile_1, smile_2]
|
1497 |
+
for i in range(2):
|
1498 |
+
speed.append(input_speed)
|
1499 |
+
eluent.append(input_eluent)
|
1500 |
+
for predict_column in column_specify.keys():
|
1501 |
+
column_descriptor = np.load('column_descriptor.npy', allow_pickle=True)
|
1502 |
+
col_specify = column_specify[predict_column]
|
1503 |
+
col_des = np.array(column_descriptor[col_specify[3]])
|
1504 |
+
mols = []
|
1505 |
+
y_pred = []
|
1506 |
+
all_descriptor = []
|
1507 |
+
dataset = []
|
1508 |
+
for smile in smiles:
|
1509 |
+
mol = Chem.MolFromSmiles(smile)
|
1510 |
+
mols.append(mol)
|
1511 |
+
for smile in smiles:
|
1512 |
+
mol = obtain_3D_mol(smile, 'conform')
|
1513 |
+
mol = Chem.MolFromMolFile(f"conform.mol")
|
1514 |
+
all_descriptor.append(mord(mol))
|
1515 |
+
dataset.append(mol_to_geognn_graph_data_MMFF3d(mol))
|
1516 |
+
|
1517 |
+
for i in range(0, len(dataset)):
|
1518 |
+
data = dataset[i]
|
1519 |
+
atom_feature = []
|
1520 |
+
bond_feature = []
|
1521 |
+
for name in atom_id_names:
|
1522 |
+
atom_feature.append(data[name])
|
1523 |
+
for name in bond_id_names[0:3]:
|
1524 |
+
bond_feature.append(data[name])
|
1525 |
+
atom_feature = torch.from_numpy(np.array(atom_feature).T).to(torch.int64)
|
1526 |
+
bond_feature = torch.from_numpy(np.array(bond_feature).T).to(torch.int64)
|
1527 |
+
bond_float_feature = torch.from_numpy(data['bond_length'].astype(np.float32))
|
1528 |
+
bond_angle_feature = torch.from_numpy(data['bond_angle'].astype(np.float32))
|
1529 |
+
y = torch.Tensor([float(speed[i])])
|
1530 |
+
edge_index = torch.from_numpy(data['edges'].T).to(torch.int64)
|
1531 |
+
bond_index = torch.from_numpy(data['BondAngleGraph_edges'].T).to(torch.int64)
|
1532 |
+
|
1533 |
+
prop = torch.ones([bond_feature.shape[0]]) * eluent[i]
|
1534 |
+
coated = torch.ones([bond_feature.shape[0]]) * col_specify[0]
|
1535 |
+
diameter = torch.ones([bond_feature.shape[0]]) * col_specify[1]
|
1536 |
+
immobilized = torch.ones([bond_feature.shape[0]]) * col_specify[2]
|
1537 |
+
|
1538 |
+
TPSA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][820] / 100
|
1539 |
+
RASA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][821]
|
1540 |
+
RPSA = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][822]
|
1541 |
+
MDEC = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][1568]
|
1542 |
+
MATS = torch.ones([bond_angle_feature.shape[0]]) * all_descriptor[i][457]
|
1543 |
+
|
1544 |
+
col_TPSA = torch.ones([bond_angle_feature.shape[0]]) * col_des[820] / 100
|
1545 |
+
col_RASA = torch.ones([bond_angle_feature.shape[0]]) * col_des[821]
|
1546 |
+
col_RPSA = torch.ones([bond_angle_feature.shape[0]]) * col_des[822]
|
1547 |
+
col_MDEC = torch.ones([bond_angle_feature.shape[0]]) * col_des[1568]
|
1548 |
+
col_MATS = torch.ones([bond_angle_feature.shape[0]]) * col_des[457]
|
1549 |
+
|
1550 |
+
bond_feature = torch.cat([bond_feature, coated.reshape(-1, 1)], dim=1)
|
1551 |
+
bond_feature = torch.cat([bond_feature, immobilized.reshape(-1, 1)], dim=1)
|
1552 |
+
bond_feature = torch.cat([bond_feature, bond_float_feature.reshape(-1, 1)], dim=1)
|
1553 |
+
bond_feature = torch.cat([bond_feature, prop.reshape(-1, 1)], dim=1)
|
1554 |
+
bond_feature = torch.cat([bond_feature, diameter.reshape(-1, 1)], dim=1)
|
1555 |
+
|
1556 |
+
bond_angle_feature = torch.cat([bond_angle_feature.reshape(-1, 1), TPSA.reshape(-1, 1)], dim=1)
|
1557 |
+
bond_angle_feature = torch.cat([bond_angle_feature, RASA.reshape(-1, 1)], dim=1)
|
1558 |
+
bond_angle_feature = torch.cat([bond_angle_feature, RPSA.reshape(-1, 1)], dim=1)
|
1559 |
+
bond_angle_feature = torch.cat([bond_angle_feature, MDEC.reshape(-1, 1)], dim=1)
|
1560 |
+
bond_angle_feature = torch.cat([bond_angle_feature, MATS.reshape(-1, 1)], dim=1)
|
1561 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_TPSA.reshape(-1, 1)], dim=1)
|
1562 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_RASA.reshape(-1, 1)], dim=1)
|
1563 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_RPSA.reshape(-1, 1)], dim=1)
|
1564 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_MDEC.reshape(-1, 1)], dim=1)
|
1565 |
+
bond_angle_feature = torch.cat([bond_angle_feature, col_MATS.reshape(-1, 1)], dim=1)
|
1566 |
+
|
1567 |
+
data_atom_bond = Data(atom_feature, edge_index, bond_feature, y)
|
1568 |
+
data_bond_angle = Data(edge_index=bond_index, edge_attr=bond_angle_feature)
|
1569 |
+
model.load_state_dict(
|
1570 |
+
torch.load(f'GeoGNN_model.pth', map_location=torch.device('cpu')))
|
1571 |
+
model.eval()
|
1572 |
+
|
1573 |
+
pred, h_graph = model(data_atom_bond.to(device), data_bond_angle.to(device))
|
1574 |
+
|
1575 |
+
y_pred.append(pred.detach().cpu().data.numpy() / speed[i])
|
1576 |
+
Prediction.append(y_pred)
|
1577 |
+
Sp.append(cal_prob(y_pred))
|
1578 |
+
Prediction_1=np.squeeze(np.array(Prediction))[:,0,1]
|
1579 |
+
Prediction_2 = np.squeeze(np.array(Prediction))[:, 1, 1]
|
1580 |
+
Sp=np.array(Sp)
|
1581 |
+
result=pd.DataFrame({'Column_name':column_specify.keys(),'RT_1':Prediction_1,'RT_2':Prediction_2,'Separation_probability':Sp})
|
1582 |
+
result= result[result.loc[:]!=0].dropna()
|
1583 |
+
result['RT_1'] = result['RT_1'].apply(lambda x: format(x, '.2f'))
|
1584 |
+
result['RT_2'] = result['RT_2'].apply(lambda x: format(x, '.2f'))
|
1585 |
+
result = result.sort_values(by="Separation_probability", ascending=False)
|
1586 |
+
result['Separation_probability'] = result['Separation_probability'].apply(lambda x: format(x, '.2%'))
|
1587 |
+
|
1588 |
+
return result
|
1589 |
+
|
1590 |
+
|
1591 |
+
|
1592 |
+
if __name__=='__main__':
|
1593 |
+
column_recommendation('CC','CCCC',0.1,0.1)
|
1594 |
+
demo_1=gr.Interface(fn=predict_separate, inputs=["text", "text", "number", "number",
|
1595 |
+
gr.Dropdown(['ADH', 'ODH', 'IC', 'IA', 'OJH', 'ASH', 'IC3',
|
1596 |
+
'IE', 'ID', 'OD3', 'IB', 'AD', 'AD3', 'IF', 'OD',
|
1597 |
+
'AS', 'OJ3', 'IG', 'AZ', 'IAH', 'OJ',
|
1598 |
+
'ICH', 'OZ3', 'IF3', 'IAU'], label="Column type",
|
1599 |
+
info="Choose a HPLC column")], outputs=['text'])
|
1600 |
+
demo_2=gr.Interface(fn=column_recommendation, inputs=["text", "text", "number", "number"],
|
1601 |
+
outputs=['dataframe'])
|
1602 |
+
demo=gr.TabbedInterface([demo_1, demo_2], ["Single prediction", "Column recommendation"])
|
1603 |
+
demo.launch()
|
1604 |
+
|
1605 |
+
|
1606 |
+
|
1607 |
+
|
1608 |
+
|
1609 |
+
|
1610 |
+
|
column_descriptor.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b263dd8713acc0b863b76ba295f5d5828b350323f4dc304115d196b9cc1fa969
|
3 |
+
size 365328
|