Dual modality feature fused neural network integrating binding site information for drug target affinity prediction.

ABSTRACT: Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced and efficient modeling of drug-target interactions. Comprehensive experiments demonstrate DMFF-DTA outperforms state-of-the-art methods with significant improvements. The model exhibits excellent generalization capabilities on completely unseen drugs and targets, achieving an improvemen

SUBMITTER: He H 

PROVIDER: S-EPMC11775287 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature