ArticleMolecules (Basel, Switzerland)2026
Drug-Target Interaction Prediction via Dual-Interaction Fusion.
Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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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 and a masked multi-scale self-attention protein encoder augmented by a narrow 1D-CNN branch for local motif aggregation. Experiments on two benchmarks,
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