DeFuseDTI: Interpretable drug target interaction prediction model with dual-branch encoder and multiview fusion

Abstract

Predicting the interaction between drugs and targets is a crucial step in drug development, and computer-based deep learning approaches have the potential to significantly reduce costs. Existing models using a single encoder often suffer from insufficient cross-modal feature extraction, with most models tending to overly focus on extracting locally aggregated information, thereby diluting the detailed features of each target residue and drug atom. Additionally, the lack of effective interaction fusion between drug and target lead to prediction results lacking reliable interpretability, posing a more urgent issue. To address these challenges, we propose a dual-branch encoder model, DeFuseDTI, which includes base encoder and detail encoder to extract locally aggregated features and detailed features of each target residue and drug atom. The detail encoder (utilizing Invertible Neural Networks for targets and graph transformers for drugs) can capture furtherly the features of each atom and residue, providing rich and precise features for model interpretability. For better achieve interactive learning of drug and target features, the Multiview Fusion Attention learning module was introduced to integrate multiview features and generate a unified representations for decoding prediction results. Based on the module's unique attention mechanism, drug-target importance matrices can be obtained, which offer interpretable analysis at the molecular level. Experimental results and analyses demonstrate that DeFuseDTI outperforms several state-of-the-art models on four public datasets. Its significant interpretability holds promise for providing accurate and scientifically meaningful guidance for biochemical experiments at the molecular level.