DCI-SiteDTA: drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block

Predicting the binding affinity between drugs and proteins is crucial for accelerating drug discovery. However, traditional research methods typically treat binding site detection and affinity prediction as two separate tasks, lacking solutions that integrate both into a unified deep learning framework. Furthermore, existing approaches exhibit limitations in two aspects: (1) for binding site detection, fine-grained features within drug target binding pockets need to be encoded; and (2) for drug-target affinity (DTA) prediction, the fusion of drug and targets needs to consider a multidimensional fusion guided by binding sites, rather than via concatenation or simple attention mechanisms. To address the aforementioned challenges, we propose a drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block(DCI-SiteDTA). Specifically, a multi-scale feature fusion is employed to extract both local and global contextual information from protein residues to get the binding site vector. Then, with a binding site-guided strategy, we introduce a dual cross-interaction fusion block. Designed to model drug-target interactions, this module constructs multilevel representations based on drug, target, and binding site information to capture their fundamental biological patterns of interaction and integration. Finally, our proposed DCI-SiteDTA model is evaluated on Davis and KIBA benchmark datasets. The experimental results reveal that our proposed model yields better accuracy on both binding site detection and DTA prediction. Our proposed model with the binding sites-guided strategy and dual cross-interaction block improves the binding site detection and affinity prediction of drug-target pairs.