Druggpt

🚀 DrugGPT

A generative drug design model based on GPT2, aiming to revolutionize drug design using natural language processing techniques.

🚀 Quick Start

DrugGPT is a generative pharmaceutical strategy based on the GPT structure. It applies the GPT model to explore the chemical space and discover new molecules with potential binding abilities for specific proteins. By training on up to 1.8 million protein - ligand binding data, it provides a fast and efficient method for generating drug candidate molecules.

✨ Features

• Innovative Approach: Utilizes natural language processing in drug design.
• Data - Driven: Trained on a large dataset of protein - ligand binding data.
• Flexible Usage: Allows input in different formats such as amino acid sequences and FASTA files.

📦 Installation

1. Clone the Repository

   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 a 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
   

💻 Usage Examples

Basic Usage

Use drug_generator.py with the following 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.

Specific Examples

• Input a Protein FASTA File

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

• Input the Amino Acid Sequence of the Protein

  python drug_generator.py -p MAKQPSDVSSECDREGRQLQPAERPPQLRPGAPTSLQTEPQGNPEGNHGGEGDSCPHGSPQGPLAPPASPGPFATRSPLFIFMRRSSLLSRSSSGYFSFDTDRSPAPMSCDKSTQTPSPPCQAFNHYLSAMASMRQAEPADMRPEIWIAQELRRIGDEFNAYYARRVFLNNYQAAEDHPRMVILRLLRYIVRLVWRMH -n 50
  

• Provide a Prompt for the Ligand

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

• Note for Linux Environment 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
  

📚 Documentation

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