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: firstname.lastname@example.org.
Bibliographical noteFunding Information:
We thank Sejeong Park for assistance with the study design and fruitful suggestions which have contributed greatly to improving and completing our work. This work was supported by the Korea Bio Data Station(K-BDS) with computing resources including technical support.
© 2023 The Author(s). Published by Oxford University Press.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics