Drug-Target Interaction Prediction via Dual-Interaction Fusion.

ABSTRACT: Accurate prediction of drug-target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal information, and modeling fine-grained drug-protein interactions. To address these challenges, We propose Gated-Attention Dual-Fusion Drug-Target Interaction (GADFDTI), whose core contribution is a fusion module that constructs an explicit atom-residue similarity field, refines it with a lightweight 2D neighborhood operator, and performs gated bidirectional aggregation to obtain interaction-aware representations. To provide strong and width-aligned unimodal inputs to this fusion module, we integrate a compact multi-scale dense GCN for drug graphs

SUBMITTER: Li X 

PROVIDER: S-EPMC12899975 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature