DrugGPT

A generative drug design model based on GPT2.

🚩 Introduction

📥 Deployment

1. Clone
   shell

   1git clone https://github.com/LIYUESEN/druggpt.git 2cd druggpt
   Or you can visit our GitHub repo and click Code>Download ZIP to download this repo.
2. Create virtual environment
   shell

   1conda create -n druggpt python=3.7 2conda activate druggpt
3. Download python dependencies
   shell

   1pip install datasets transformers scipy scikit-learn 2pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 3conda install -c openbabel openbabel

🗝 How to use

• -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

⚖ License