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. Qualita

SUBMITTER: Gim M 

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

REPOSITORIES: biostudies-literature