MF-DTA: Predicting drug–target affinity with multi-modal feature fusion model

The prediction of drug–target interactions (DTIs) and binding affinities (DTAs) plays a pivotal role in drug discovery and design. However, most existing methods fail to fully exploit the rich multimodal information inherent in molecular structures. In this study, we propose a multimodal feature fusion model, MF-DTA. On the representational level, MF-DTA introduces the molecular fragment graph, generated via BRICS-based decomposition, as a novel modality. This representation enables a more intuitive capture of the structural characteristics and pharmacophore-related information of drug molecules. In terms of model architecture, a deformable convolutional layer is applied for the protein residue–residue contact map (hereafter referred to as contact map) to flexibly adjust the distribution of sampling points and enhance the representational capability. To effectively integrate the multimodal information from both drug and target branches, a mixture-of-experts (MoE)-based multihead attention mechanism is employed for local fusion, while a dual-decoder architecture facilitates cross-modal interaction between drug and target features. The final output yields a high-quality prediction of binding affinity. Cross-validation experiments conducted on several benchmark datasets demonstrate that MF-DTA consistently outperforms state-of-the-art methods. Specifically, it achieves CI improvements of 0.1%, 0.5%, and 0.3% over the best-performing baseline models in the Davis, KIBA and BindingDB datasets, respectively, and exceeds traditional models by 1% to 2% on average. The model also ranks among the best performers in terms of the MSE and R_m ${}^2$ metrics. Model visualization further supports its interpretability, confirming that it successfully learns meaningful drug–target interaction patterns.To further assess the practical utility of the proposed model, we apply it to screen potential candidate compounds from a natural product library targeting tubulin. In summary, MF-DTA offers not only accurate and robust binding affinity prediction capabilities but also strong interpretability, making it a powerful and practical tool for drug design and target identification.