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gokceuludogan/
ChemBERTaLM

Name: ChemBERTaLM
Availability: InStock

biochemical-language-models

mit

autotrain_compatible

endpoints_compatible

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ChemBERTaLM

A molecule generator model finetuned from ChemBERTa checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for targeted drug design", which has been accepted for publication in Bioinformatics Published by Oxford University Press and first released in this repository.

ChemBERTaLM is a RoBERTa model initialized with ChemBERTa checkpoint, and then, finetuned on the MOSES dataset which comprises a collection of drug-like compounds.

How to use

python

from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline
tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
generator("", max_length=128, do_sample=True)
# Sample output
[{'generated_text': 'Cc1ccc(C(=O)N2CCN(C(=O)c3ccc(F)cc3)CC2)cc1'}]

Citation

bibtex

@article{10.1093/bioinformatics/btac482,
    author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan},
    title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}",
    journal = {Bioinformatics},
    year = {2022},
    doi = {10.1093/bioinformatics/btac482},
    url = {https://doi.org/10.1093/bioinformatics/btac482}
}