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--- |
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license: mit |
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inference: false |
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tags: |
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- molecule-generation |
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- cheminformatics |
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- targeted-drug-design |
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- biochemical-language-models |
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--- |
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## WarmMolGenOne |
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A target specific molecule generator model which is warm started (i.e. initialized) from pretrained biochemical language models and trained on interacting protein-compound pairs, viewing targeted molecular generation as a translation task between protein and molecular languages. It was introduced in the paper, "Exploiting pretrained biochemical language models for |
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targeted drug design", which has been accepted for publication in *Bioinformatics* Published by Oxford University Press and first released in [this repository](https://github.com/boun-tabi/biochemical-lms-for-drug-design). |
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WarmMolGenOne is a Transformer-based encoder-decoder model initialized with [Protein RoBERTa](https://github.com/PaccMann/paccmann_proteomics) and [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoints and trained on interacting protein-compound pairs filtered from [BindingDB](https://www.bindingdb.org/rwd/bind/index.jsp). The model takes a protein sequence as an input and outputs a SMILES sequence. |
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## How to use |
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```python |
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from transformers import EncoderDecoderModel, RobertaTokenizer, pipeline |
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protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenOne") |
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mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k") |
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model = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenOne") |
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inputs = protein_tokenizer("MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTG", >>> return_tensors="pt") |
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outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id, |
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eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id, |
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max_length=128, num_return_sequences=5, do_sample=True, top_p=0.95) |
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mol_tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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# Sample output |
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# ['Cn1cc(nn1)-c1ccccc1NS(=O)(=O)c1ccc2[nH]ccc2c1', |
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# 'CC(C)(C)c1[se]nc2sc(cc12)C(O)=O', |
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# '[O-][N+](=O)c1ccc(CN2CCC(CC2)NC(=O)c2cccc3ccccc23)cc1', |
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# 'OC(=O)CNC(=O)CCC\\C=C\\CN1[C@@H](Cc2cn(nn2)-c2ccccc2)C(=O)N[C@@H](CCCN2C(S)=NC(C)(C2=O)c2ccc(F)cc2)C1=O', |
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# 'OCC1(CCC1)C(=O)NCC1CCN(CC1)c1nc(c(s1)-c1ccc2OCOc2c1)C(O)=O'] |
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``` |
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## Citation |
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```bibtex |
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@article{10.1093/bioinformatics/btac482, |
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author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan}, |
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title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}", |
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journal = {Bioinformatics}, |
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year = {2022}, |
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doi = {10.1093/bioinformatics/btac482}, |
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url = {https://doi.org/10.1093/bioinformatics/btac482} |
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} |
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``` |
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