Dataset Information

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.

ABSTRACT:

Motivation

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 (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein-ligand attention mechanism for more explainable deep drug-target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs.

Results

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 NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner.

Availability

ArkDTA is available at https://github.com/dmis-lab/ArkDTA.

Contact

kangj@korea.ac.kr.

SUBMITTER: Gim M

PROVIDER: S-EPMC10311339 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.

Gim Mogan M Choe Junseok J Baek Seungheun S Park Jueon J Lee Chaeeun C Ju Minjae M Lee Sumin S Kang Jaewoo J

Bioinformatics (Oxford, England) 20230601 39 Suppl 1

<h4>Motivation</h4>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 (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein-ligand attention mechanism for more explainable deep drug-target interaction models. We ...[more]

PMID: 37387164

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Project description:Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction. The first difficulty lies in the phenomenon of sample mismatch: self-supervised learning processes drug and target samples independently, while actual prediction requires the integration of drug-target pairs. Another challenge is the mismatch between the broadness of self-supervised learning objectives and the precision of biological mechanisms of drug-target affinity (i.e., the induced-fit principle). The former focuses on global feature extraction, while the latter emphasizes the importance of local precise matching. To address these issues, an adaptive self-supervised learning-based drug-target affinity prediction (ASSLDTA) was designed. ASSLDTA integrates a novel adaptive self-supervised learning (ASSL) module with a high-level feature learning network to extract the feature. The ASSL leverages a large amount of unlabeled training data to effectively capture low-level features of drugs and targets. Its goal is to maximize the retention of original feature information, thereby bridging the objective gap between self-supervised learning and drug-target affinity prediction and alleviating the sample mismatch problem. The high-level feature learning network, on the other hand, focuses on extracting effective high-level features for affinity prediction through a small amount of labeled data. Through this two-stage feature extraction design, each stage undertakes specific tasks, fully leveraging the advantages of each model while efficiently integrating information from different data sources, providing a more accurate and comprehensive solution for drug-target affinity prediction. In our experiments, ASSLDTA is much better than other deep methods, and the result of ASSLDTA is significantly increased by learning adaptive self-supervised learning-based features, which validates the effectiveness of our ASSLDTA.

Dataset Information

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.

Motivation

Results

Availability

Contact

Publications

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.

Similar Datasets

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