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] [Project Page] [ArXiv]

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)

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:

conda create -n ChatDrug python=3.7
conda activate ChatDrug
conda install -y -c rdkit rdkit
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 -e .

Dataset

We provide the dataset in this link. 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.

Evaluation

The evaluation metrics for three editing tasks are below:

Drug Type	Evaluation
Small Molecule	RDKit (conda install -y -c rdkit rdkit)
Peptide	MHCFlurry
Protein	ProteinDT paper, checkpoints

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.

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.

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}
}