DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction

Drug-target binding affinity (DTA) is central to computer-aided drug design. Although biochemical assays yield accurate measurements, their high cost and inefficiency limit scalability. Computational approaches, particularly deep learning, have recently shown promising progress in accelerating DTA prediction and reducing experimental burden. However, existing methods rarely incorporate biochemical knowledge, which limits their performance. To address this issue, we propose a deep learning framework termed DTANet+ for drug-target affinity prediction, which is inspired by the biochemical properties of drugs and targets. Specifically, given that functional groups and peptide chains, which vary significantly in length, are crucial in determining drug and target properties, we propose a Kernel-diverse Feature Extraction Block (KFEB) and Cross-Scale Interaction Module (CSIM) to extract drug and target features. Inspired by the crucial role of drug-target binding sites in precise affinity prediction, we introduce the Drug-Target Interaction Module (DTIM) to explore drug-target relationships and focus on binding sites. Besides, we design a Multi-modal Fusion Module (MFM) to effectively integrate the complementary information across different modalities and conduct prediction. Extensive experiments on the KIBA and Davis datasets show that DTANet+ consistently outperforms existing methods, achieving higher Concordance Index (CI) and lower Mean Squared Error (MSE). These results confirm the effectiveness of DTANet+ and highlight its potential for drug repositioning and personalized medicine. The source code is publicly available at: https://github.com/202324131016T/DTANet_plus .