# DrugGPT
A generative drug design model based on GPT2.
<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">
## 🚩 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
    ```shell
    git clone https://github.com/LIYUESEN/druggpt.git
    cd druggpt
    ```
   Or you can visit our [GitHub repo](https://github.com/LIYUESEN/druggpt) and click *Code>Download ZIP* to download this repo.
2. Create virtual environment
    ```shell
    conda create -n druggpt python=3.7
    conda activate druggpt
    ```
3. Download python dependencies
    ```shell
    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](https://github.com/LIYUESEN/druggpt/blob/main/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
    ```shell
    python drug_generator.py -f bcl2.fasta -n 50
    ```
- If you want to input the amino acid sequence of the protein
    ```shell
    python drug_generator.py -p MAKQPSDVSSECDREGRQLQPAERPPQLRPGAPTSLQTEPQGNPEGNHGGEGDSCPHGSPQGPLAPPASPGPFATRSPLFIFMRRSSLLSRSSSGYFSFDTDRSPAPMSCDKSTQTPSPPCQAFNHYLSAMASMRQAEPADMRPEIWIAQELRRIGDEFNAYYARRVFLNNYQAAEDHPRMVILRLLRYIVRLVWRMH -n 50
    ```
    
- If you want to provide a prompt for the ligand  
    ```shell
    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 ('').  
    ```shell
    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](https://doi.org/10.1101/2023.06.29.543848)

[![DOI](https://img.shields.io/badge/DOI-10.1101/2023.06.29.543848-blue)](https://doi.org/10.1101/2023.06.29.543848)
## ⚖ License
[GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html)
{"license": "gpl-3.0", "tags": ["chemistry", "biology", "medical", "gpt2"]}
transformers pytorch gpt2 text-generation chemistry biology medical doi:10.57967/hf/0627 license:gpl-3.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #chemistry #biology #medical #doi-10.57967/hf/0627 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

# DrugGPT
A generative drug design model based on GPT2.
<img src="URL src="URL src="URL
##  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
    
   Or you can visit our GitHub repo and click *Code>Download ZIP* to download this repo.
2. Create virtual environment
    
3. Download python dependencies
    
##  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
    
- If you want to input the amino acid sequence of the protein
    
    
- If you want to provide a prompt for the ligand  
    
    
- Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes ('').  
    
##  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: URL

![DOI](URL
##  License
GNU General Public License v3.0
# DrugGPT
A generative drug design model based on GPT2.
<img src="URL src="URL src="URL ##  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
    
   Or you can visit our GitHub repo and click *Code>Download ZIP* to download this repo.
2. Create virtual environment
    
3. Download python dependencies ##  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
    
- If you want to input the amino acid sequence of the protein
    
    
- If you want to provide a prompt for the ligand  
    
    
- Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes (''). ##  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: URL

![DOI](URL ##  License
GNU General Public License v3.0
TAGS
#transformers #pytorch #gpt2 #text-generation #chemistry #biology #medical #doi-10.57967/hf/0627 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # DrugGPT
A generative drug design model based on GPT2.
<img src="URL src="URL src="URL ##  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
    
   Or you can visit our GitHub repo and click *Code>Download ZIP* to download this repo.
2. Create virtual environment
    
3. Download python dependencies ##  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
    
- If you want to input the amino acid sequence of the protein
    
    
- If you want to provide a prompt for the ligand  
    
    
- Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes (''). ##  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: URL

![DOI](URL ##  License
GNU General Public License v3.0
TAGS
#transformers #pytorch #gpt2 #text-generation #chemistry #biology #medical #doi-10.57967/hf/0627 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# DrugGPT
A generative drug design model based on GPT2.
<img src="URL src="URL src="URL##  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
    
   Or you can visit our GitHub repo and click *Code>Download ZIP* to download this repo.
2. Create virtual environment
    
3. Download python dependencies##  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
    
- If you want to input the amino acid sequence of the protein
    
    
- If you want to provide a prompt for the ligand  
    
    
- Note: If you are running in a Linux environment, you need to enclose the ligand's prompt with single quotes ('').##  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: URL

![DOI](URL##  License
GNU General Public License v3.0