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--- |
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license: gpl-3.0 |
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tags: |
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- chemistry |
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- biology |
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- medical |
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- gpt2 |
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--- |
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# DrugGPT |
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A generative drug design model based on GPT2. |
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<img src="https://img.shields.io/github/license/LIYUESEN/druggpt"><img src="https://img.shields.io/badge/python-3.7-blue"><img src="https://img.shields.io/github/stars/LIYUESEN/druggpt?style=social"> |
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## 🚩 Introduction |
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DrugGPT is a generative pharmaceutical strategy based on GPT structure, which aims to bring innovation to drug design by using natural language processing technique. |
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This project applies the GPT model to the exploration of chemical space to discover new molecules with potential binding abilities for specific proteins. |
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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. |
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## 📥 Deployment |
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1. Clone |
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```shell |
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git clone https://github.com/LIYUESEN/druggpt.git |
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cd druggpt |
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``` |
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Or you can visit our [GitHub repo](https://github.com/LIYUESEN/druggpt) and click *Code>Download ZIP* to download this repo. |
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2. Create virtual environment |
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```shell |
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conda create -n druggpt python=3.7 |
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conda activate druggpt |
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``` |
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3. Download python dependencies |
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```shell |
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pip install datasets transformers scipy scikit-learn |
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 |
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conda install -c openbabel openbabel |
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``` |
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## 🗝 How to use |
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Use [drug_generator.py](https://github.com/LIYUESEN/druggpt/blob/main/drug_generator.py) |
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Required parameters: |
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- `-p` | `--pro_seq`: Input a protein amino acid sequence. |
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- `-f` | `--fasta`: Input a FASTA file. |
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> Only one of -p and -f should be specified. |
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- `-l` | `--ligand_prompt`: Input a ligand prompt. |
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- `-e` | `--empty_input`: Enable directly generate mode. |
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- `-n` | `--number`: At least how many molecules will be generated. |
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- `-d` | `--device`: Hardware device to use. Default is 'cuda'. |
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- `-o` | `--output`: Output directory for generated molecules. Default is './ligand_output/'. |
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- `-b` | `--batch_size`: How many molecules will be generated per batch. Try to reduce this value if you have low RAM. Default is 32. |
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## 🔬 Example usage |
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- If you want to input a protein FASTA file |
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```shell |
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python drug_generator.py -f bcl2.fasta -n 50 |
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``` |
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- If you want to input the amino acid sequence of the protein |
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```shell |
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python drug_generator.py -p MAKQPSDVSSECDREGRQLQPAERPPQLRPGAPTSLQTEPQGNPEGNHGGEGDSCPHGSPQGPLAPPASPGPFATRSPLFIFMRRSSLLSRSSSGYFSFDTDRSPAPMSCDKSTQTPSPPCQAFNHYLSAMASMRQAEPADMRPEIWIAQELRRIGDEFNAYYARRVFLNNYQAAEDHPRMVILRLLRYIVRLVWRMH -n 50 |
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``` |
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- If you want to provide a prompt for the ligand |
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```shell |
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python drug_generator.py -f bcl2.fasta -l COc1ccc(cc1)C(=O) -n 50 |
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``` |
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- Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes (''). |
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```shell |
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python drug_generator.py -f bcl2.fasta -l 'COc1ccc(cc1)C(=O)' -n 50 |
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``` |
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## 📝 How to reference this work |
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DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins |
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Yuesen Li, Chengyi Gao, Xin Song, Xiangyu Wang, Yungang Xu, Suxia Han |
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bioRxiv 2023.06.29.543848; doi: [https://doi.org/10.1101/2023.06.29.543848](https://doi.org/10.1101/2023.06.29.543848) |
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[](https://doi.org/10.1101/2023.06.29.543848) |
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## ⚖ License |
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[GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) |
