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--- |
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license: mit |
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tags: |
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- molecule-generation |
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- cheminformatics |
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- biochemical-language-models |
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widget: |
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- text: "c1ccc2c(c1)" |
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example_title: "Scaffold Hopping" |
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--- |
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## ChemBERTaLM |
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A molecule generator model finetuned from [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint. 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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ChemBERTaLM is a RoBERTa model initialized with [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint, and then, finetuned on the MOSES dataset which comprises a collection of drug-like compounds. |
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## How to use |
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```python |
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from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline |
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tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM") |
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model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM") |
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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generator("", max_length=128, do_sample=True) |
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# Sample output |
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[{'generated_text': 'Cc1ccc(C(=O)N2CCN(C(=O)c3ccc(F)cc3)CC2)cc1'}] |
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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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``` |
