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https://github.com/chao1224/ChatDrug

LLM for Drug Editing, ICLR 2024
https://github.com/chao1224/ChatDrug

chatgpt chatgpt3 conversation domain-feedback drug drug-discovery drug-editing editing llm molecule motif peptide protein retrieval secondary-structure small-molecule structure

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LLM for Drug Editing, ICLR 2024

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README 

        
# Conversational Drug Editing Using Retrieval and Domain Feedback



**ICLR 2024**



Authors: Shengchao Liu+, Jiongxiao Wang+, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo\*, Chaowei Xiao\*



+ Equal contribution


\* Equal advising



[[Paper](https://openreview.net/forum?id=yRrPfKyJQ2)]

[[Project Page](https://chao1224.github.io/ChatDrug)]

[[ArXiv](https://arxiv.org/abs/2305.18090)]





   




ChatDrug is for conversational drug editing, and three types of drugs are considered:

- Small Molecules

- Peptides

- Proteins



   




## Environment



Setup the anaconda (skip this if you already have conda)

 ```bash

wget https://repo.continuum.io/archive/Anaconda3-2019.10-Linux-x86_64.sh

bash Anaconda3-2019.10-Linux-x86_64.sh -b

export PATH=$PWD/anaconda3/bin:$PATH

```



Then download the required python packages:

```bash

conda create -n ChatDrug python=3.8

conda activate ChatDrug

pip install rdkit-pypi==2022.9.4

conda install -y numpy networkx scikit-learn

conda install -y -c conda-forge -c pytorch pytorch=1.9.1



pip install tensorflow

pip install mhcflurry

pip install levenshtein



pip install transformers

pip install lmdb

pip install seqeval

pip install openai

pip install fastchat

pip install psutil

pip install accelerate



pip install -e .

```



## Dataset



We provide the dataset in [this link](https://huggingface.co/datasets/chao1224/ChatDrug_data). You can manually download and move to the `data` folder or using the following python script.

```

from huggingface_hub import snapshot_download



snapshot_download(repo_id="chao1224/ChatDrug_data", repo_type="dataset", local_dir="data", local_dir_use_symlinks=False, ignore_patterns=["README.md"])

```

Please give credits to the original papers. For more details of dataset, please check the [data folder](./data).



## Evaluation



The evaluation metrics for three editing tasks are below:

| Drug Type | Evaluation |

| -- | -- |

| Small Molecule | RDKit (`conda install -y -c rdkit rdkit`)|

| Peptide | [MHCFlurry](https://github.com/openvax/mhcflurry)|

| Protein | [ProteinDT paper](https://arxiv.org/abs/2302.04611), [checkpoints](https://huggingface.co/chao1224/ProteinCLAP_pretrain_EBM_NCE_downstream_property_prediction) |



For evaluation on peptides and proteins, please read the following instructions:

- For peptides (MHCFlurry), please run the following bash commands:

```

> pip install mhcflurry

> mhcflurry-downloads fetch models_class1_presentation

> mhcflurry-downloads path models_class1_presentation

$PATH

> mv $PATH data/peptide/models_class1_presentation

```

- For proteins (ProteinDT / ProteinCLAP), please run the following python script:

```

from huggingface_hub import hf_hub_download



hf_hub_download(

  repo_id="chao1224/ProteinCLAP_pretrain_EBM_NCE_downstream_property_prediction",

  repo_type="model",

  filename="pytorch_model_ss3.bin",

  cache_dir="data/protein")

```

Please give credits to the original papers. For more details of evaluation, please check the [data folder](./data).



## Prompt for Drug Editing



All the task prompts are defined in `ChatDrug/task_and_evaluation`. you can also find it on [the hugging face link](https://huggingface.co/datasets/chao1224/ChatDrug_prompt).



## Usage



Please provide your OpenAI API Key in `ChatDrug/task_and_evaluation/Conversational_LLMs_utils.py`



To use ChatDrug, please use the following command:

```

python main_ChatDrug.py --task task_id --log_file results/ChatDrug.log --record_file results/ChatDrug.json --C 2

```

Results will be saved in `results/`.



For protein editing tasks, multiple evaluation times in retrieval process would consume a lot of time. Thus, we provide a fast version of conversation setting. Running the following command to implement accelerate ChatDrug for protein editing tasks:

```

python main_ChatDrug.py --task task_id --log_file results/ChatDrug_fast_protein.log --record_file results/ChatDrug_fast_protein.json --C 2 --fast_protein

```



We also provide code for In-Context Learning setting:

```

python main_InContext.py --task task_id --log_file results/InContext.log --record_file results/InContext.json

```



## Cite Us

Feel free to cite this work if you find it useful to you!



```

@inproceedings{liu2024chatdrug,

    title={Conversational Drug Editing Using Retrieval and Domain Feedback},

    author={Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao},

    booktitle={The Twelfth International Conference on Learning Representations},

    year={2024},

    url={https://openreview.net/forum?id=yRrPfKyJQ2}

}

```