MLDTA an Ensemble-Driven Multimodal Model with Dynamic Fusion for Drug-Target Affinity Prediction

Existing drug-target binding affinity (DTA) models still face two major challenges. First, current multimodal approaches often rely on fixed fusion strategies or single model architectures, which limits their ability to adaptively capture the complex and heterogeneous relationships between drugs and targets. Second, heavy dependence on a single learning algorithm reduces model robustness and generalization, resulting in persistently large prediction errors. We propose MLDTA, a multimodal framework for DTA prediction that integrates dynamic feature fusion and ensemble-inspired modeling principles. MLDTA jointly exploits structural information, Geary autocorrelation descriptors, and tripeptide composition to construct complementary drug and target representations. Instead of relying on a single predictor, five representative DTA models from the literature are incorporated as auxiliary predictive modules (APMs), enabling affinity prediction from multiple algorithmic perspectives. These APMs are integrated with the learned drug and target representations through a dynamic fusion mechanism based on attention modules, which adaptively learns the relative importance of different features and predictive signals, thereby enhancing cross-modal interaction and reducing dependence on any individual model. Evaluation on standard datasets indicates that our model surpasses existing methods. Case studies further highlight MLDTA's effectiveness in drug screening.