https://github.com/dmis-lab/arkdta
https://github.com/dmis-lab/arkdta
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/dmis-lab/arkdta
- Owner: dmis-lab
- Created: 2023-03-29T04:29:47.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-04-11T13:40:45.000Z (about 3 years ago)
- Last Synced: 2025-07-14T20:10:05.442Z (11 months ago)
- Language: Python
- Size: 3.45 MB
- Stars: 11
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ArkDTA: Attention Regularization guided by non-Covalent Interactions for Explainable Drug-Target Binding Affinity Prediction
## Abstract
Protein-ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions, one of the most critical domain knowledge in binding affinity prediction task, should be incorporated in protein-ligand attention mechanism for more explainable deep DTI models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by non-covalent interactions. Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for non-covalent interactions between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner. (*submitted to ISMB2023, under review*)
## Overview of ArkDTA
## Attention Regularization guided by non-Covalent Interactions
## Prerequisites for running ArkDTA
- Python 3.7.9
- CUDA: 11.X
- Download and extract data.tar.gz ([link](https://drive.google.com/file/d/1hmR5w47VUk6RW0br8BanJT94R2FPHgDL/view?usp=share_link)), 45MB) at current directory. These files are the preprocessed datasets PDBBind (ver.2020), Davis and Metz.
- Download and extract saved.tar.gz ([link](https://drive.google.com/file/d/1iVttdzlAMXYeJ11JKVe19Dkvgpb8PZSS/view?usp=share_link)), 170MB) at directory **./saved**. These files are the model checkpoints for each fold of the PDBbind datset.
## Installing the Python (3.8.12) Conda Environment
```
conda env create -f arkdta.yaml
conda activate arkdta
```
## How to use the ArkDTA source code
### Training ArkDTA on PDBBind Dataset
Run the following code,
```
python run.py -pn {wandb_project_name} -sn arkdta -mg {multiple gpu indices}
```
If you want to train ArkDTA on the IC50 subset, configure the **/sessions/arkdta.yaml** by editing the following,
```
ba_measure: IC50
```
### Evaluating ArkDTA on PDBBind Dataset (5CV)
Run the following code,
```
python run.py -pn {wandb_project_name} -sn arkdta -mg {multiple gpu indices} -tm
```
### Finetuning ArkDTA on other datasets (Davis, Metz)
Configure the **/sessions/arkdta.yaml** by editing the following,
```
dataset_subsets: davis
dataset_partition: randomsingle
```
Then run the following code,
```
python run.py -pn {wandb_project_name} -sn arkdta -mg {multiple gpu indices} -ft {davis or metz}
```
### Evaluating ArkDTA on other datasets
Run the following code,
```
python run.py -pn {wandb_project_name} -sn arkdta -mg {multiple gpu indices} -tm -cn {your/saved/path_davis or _metz}
```
### Running model inference and extracting attention maps from ArkDTA
Run the following script,
```
./arkdta.sh
```
You can change the input SMILES (ligands) or FASTA sequence (proteins) by editting the **arkdta.sh** file.
#### 4x6n, 3Y5
#### 6n77, KEJ
#### 8bq4, QZR
## Contributors
Name
Affiliation
Email
Mogan Gim
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
akim@korea.ac.kr
Junseok Choe
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
juns94@korea.ac.kr
Seungheun Baek
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
tmdgms9417@korea.ac.kr
Jueon Park
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
jueon_park@korea.ac.kr
Chaeeun Lee
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
chaeeunlee1997@korea.ac.kr
Minjae Ju†
LG CNS, AI Research Center, Seoul, South Korea
minjae.ju@lgcns.com
Sumin Lee†
LG AI Research, Seoul South Korea
sumin.lee@lgresearch.ai
Jaewoo Kang*
Data Mining and Information Systems Lab,
Korea University, Seoul, South Korea
kangj@korea.ac.kr
- †: *This work was done while the author was a graduate student at Korea University Computer Science Department.*
- *: *Corresponding Author*