readme: ---
  license: mit
  tags:
  
  - molecule-generation
  - cheminformatics
  - biochemical-language-models
  
  widget:
  - text: "c1ccc2c(c1)"
    example_title: "Scaffold Hopping"
  ---
  
  ## ChemBERTaLM
  
  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
  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).
  
  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. 
  
  ## 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}
  }
  ```