DICCA-DTA: Diffusion and Contextualized Capsule Attention guided Factorized Cross-Pooling for Drug-Target Affinity prediction

Abstract

Drug-Target Affinity (DTA) prediction plays a crucial role in the drug discovery process by evaluating the strength of the interaction between a drug and its biological target, which is often a protein. Despite advancements in DTA prediction through deep learning, several fundamental challenges persist: (i) suboptimal information propagation in molecular graphs, limiting the effective representation of complex drug structures, (ii) accurately modeling the complex interactions between drug-binding sites and protein substructures, and (iii) prioritizing critical substructure interactions to enhance both accuracy and interpretability. To address these challenges, the DICCA-DTA framework is introduced, aiming to improve the contextual integration of molecular information and facilitate a more comprehensive representation of drug-target interactions in allopathic research. It employs a Diffused Isomorphic Network (DIN) to extract comprehensive drug features from molecular graphs, capturing both local substructures and global information. Furthermore, a Contextualized Capsule Attention Network (CCAN) module incorporates multi-head attention with capsule networks to capture both local and global protein sequence characteristics. The attention-guided Factorized Cross-Pooling (FCP) mechanism dynamically refines drug-protein interaction modeling by selectively emphasizing critical binding site interactions, thereby enhancing predictive accuracy. Explainable attention maps further reveal the most crucial drug-protein binding site interactions, providing transparent insights into the model’s decision-making process. Comprehensive evaluations across the Davis, KIBA, Metz and BindingDB datasets demonstrate the superior performance of the DICCA-DTA framework over existing state-of-the-art models. A case study on cancer-related protein interactions from the DrugBank database further demonstrates the framework’s precision in identifying key drug-protein affinities, reinforcing its potential to accelerate drug discovery and repurposing.