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Model: liyuesen/druggpt
Source: Original Platform

.gitattributes

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README.md

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+---
+license: gpl-3.0
+tags:
+- chemistry
+- biology
+- medical
+- gpt2
+---
+# 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)

added_tokens.json

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config.json

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+{
+  "_name_or_path": "../model_save/epoch_4",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
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+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.2",
+  "use_cache": true,
+  "vocab_size": 53083
+}

generation_config.json

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+  "eos_token_id": 50256,
+  "transformers_version": "4.27.2"
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special_tokens_map.json

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tokenizer_config.json

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