ArticleBioinformatics (Oxford, England)2023
ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- LINKER:Journal of chemical information and modeling · 2026Article
- ArcDFI: Attention regularization guided by CYP450 interactions for predicting drug-food interactions.PLoS computational biology · 2025Article
- BADGER: biologically-aware interpretable differential gene expression ranking model.Bioinformatics advances · 2025Article
- DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction.Journal of cheminformatics · 2024Article
- Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations.Bioinformatics (Oxford, England) · 2024Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints.Bioinformatics (Oxford, England) · 2024Article
- Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.PloS one · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
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Authors and funding
8 authors.
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Abstract
motivationProtein-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.
resultsExperimental 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.
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