MMTF-DTI: Drug-target interaction prediction via multimodal feature extraction and dynamic fusion.

Abstract

Drug-target interaction (DTI) prediction is vital for computer-aided drug discovery, yet current methods struggle with cross-domain generalization and multimodal fusion. To address these challenges, we propose MMTF-DTI, a multimodal framework that establishes deep interaction between drug and target representations via multimodal feature extraction and a Transformer-based fusion mechanism. Specifically, we design a dual-path encoder: Transformers decode semantic information from drug SMILES and protein sequences, while graph neural networks capture structural features of molecules and proteins. We then introduce a dynamic multimodal fusion module with learnable attention weights for adaptive feature interaction, enhancing compound-target representation learning. To further improve generalization, a domain adversarial neural network is incorporated to mitigate cross-domain distribution shifts. Comprehensive evaluations on benchmark datasets show that MMTF-DTI achieves state-of-the-art performance, with average gains of 1.7% AUROC and 2.1% AUPR over baselines. In cross-domain adaptation, the model also exhibits superior transferability, highlighting its potential for large-scale drug discovery where reliable cross-domain prediction is crucial.