Update README.md (#6)

- Update README.md (02ef01255cc09c688bf7ecd4cd4bd35ed63545b1)


Co-authored-by: XJTU_WangXiangyu <wxyedward@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,5 @@
 ---
-license: artistic-2.0
+license: gpl-3.0
 tags:
 - chemistry
 - biology
@@ -8,8 +8,14 @@ tags:
 ---
 # DrugGPT
 A generative drug design model based on GPT2.
-<img src="https://img.shields.io/badge/license-Artistic%20License%202.0-green"><img src="https://img.shields.io/badge/python-3.7-blue"><img src="https://img.shields.io/github/stars/LIYUESEN/druggpt?style=social">
-## Deployment
+<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
@@ -27,8 +33,21 @@ A generative drug design model based on GPT2.
     pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
     conda install -c openbabel openbabel
     ```
-## Example usage
-Run the script with the desired arguments, such as the protein sequence, ligand prompt, number of molecules to generate, and output directory.  
+## 🗝 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
@@ -42,9 +61,18 @@ Run the script with the desired arguments, such as the protein sequence, 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
-    ```  
-## License
-[Artistic License 2.0](https://opensource.org/license/artistic-license-2-0-php/)
+    ```
+## 📝 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)