MolRes-DTA: a molecular-multiview fusion and residue-aware model for drug–target affinity prediction

Abstract

Accurate prediction of drug–target affinity (DTA) is crucial for drug screening and reducing drug development costs. Despite the significant progress made by deep learning methods in DTA prediction, most existing approaches neglect two critical factors: the influence of drug molecular size and the different contribution of amino acid residues to DTA. Here, we propose an affinity prediction model, MolRes-DTA, which introduces multiview drug characterization and a dynamic residue-aware network to capture the influence of molecular size on affinity prediction and weigh the contributions of different residues. Experiments on the Davis and KIBA datasets demonstrate that MolRes-DTA outperforms baseline models by 15.58% and 20.11%, respectively. Further analysis shows that our multiview drug representation improves prediction accuracy across different types of molecular sizes, with particularly notable gains for larger compounds. To our knowledge, this is the first study to explore the impact of molecular size on DTA prediction, providing a novel perspective for enhancing the accuracy of DTA prediction.