DrugGPT

A generative drug design model based on GPT2.

🚩 Introduction

DrugGPT is a generative pharmaceutical strategy based on GPT structure, which aims to bring innovation to drug design by using natural language processing technique.

This project applies the GPT model to the exploration of chemical space to discover new molecules with potential binding abilities for specific proteins.

DrugGPT provides a fast and efficient method for the generation of drug candidate molecules by training on up to 1.8 million protein-ligand binding data.

📥 Deployment

1. Clone

   git clone https://github.com/LIYUESEN/druggpt.git
   cd druggpt
   

   Or you can visit our GitHub repo and click Code>Download ZIP to download this repo.
2. Create virtual environment

   conda create -n druggpt python=3.7
   conda activate druggpt
   

3. Download python dependencies

   pip install datasets transformers scipy scikit-learn
   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
   conda install -c openbabel openbabel
   

🗝 How to use

Use drug_generator.py

Required parameters:

• -p | --pro_seq: Input a protein amino acid sequence.

• -f | --fasta: Input a FASTA file.

  Only one of -p and -f should be specified.

• -l | --ligand_prompt: Input a ligand prompt.

• -e | --empty_input: Enable directly generate mode.

• -n | --number: At least how many molecules will be generated.

• -d | --device: Hardware device to use. Default is 'cuda'.

• -o | --output: Output directory for generated molecules. Default is './ligand_output/'.

• -b | --batch_size: How many molecules will be generated per batch. Try to reduce this value if you have low RAM. Default is 32.

🔬 Example usage

• If you want to input a protein FASTA file

  python drug_generator.py -f bcl2.fasta -n 50
  

• If you want to input the amino acid sequence of the protein

  python drug_generator.py -p MAKQPSDVSSECDREGRQLQPAERPPQLRPGAPTSLQTEPQGNPEGNHGGEGDSCPHGSPQGPLAPPASPGPFATRSPLFIFMRRSSLLSRSSSGYFSFDTDRSPAPMSCDKSTQTPSPPCQAFNHYLSAMASMRQAEPADMRPEIWIAQELRRIGDEFNAYYARRVFLNNYQAAEDHPRMVILRLLRYIVRLVWRMH -n 50
  

• If you want to provide a prompt for the ligand

  python drug_generator.py -f bcl2.fasta -l COc1ccc(cc1)C(=O) -n 50
  

• Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes ('').

  python drug_generator.py -f bcl2.fasta -l 'COc1ccc(cc1)C(=O)' -n 50
  

📝 How to reference this work

DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins

Yuesen Li, Chengyi Gao, Xin Song, Xiangyu Wang, Yungang Xu, Suxia Han

bioRxiv 2023.06.29.543848; doi: https://doi.org/10.1101/2023.06.29.543848

⚖ License

GNU General Public License v3.0