MDCDR: predicting cancer drug response via multimodal feature fusion and feature disentanglement.

Due to the heterogeneity in cancer patients, accurate prediction of cancer drug response is key to achieving personalized medicine. The biological differences between cell lines and tumor tissues, as well as the domain shift caused by diverse drug mechanisms of action, make it difficult for existing methods to effectively capture the complex relationships between multimodal features. In this study, we propose a multimodal feature fusion and feature disentanglement model, named MDCDR. The model effectively integrates various modalities of drugs and cell lines. By employing an interaction-level feature disentanglement framework that integrates multi-granularity contrastive learning and feature masking prediction, we successfully achieve effective synergy between shared common patterns and specific preserved patterns at the drug-cell line interaction level. Experiments show that MDCDR significantly outperforms existing methods on benchmarks and independent clinical test sets, and exhibits good generalization to new cell lines and drugs. Furthermore, case studies on Acute Myeloid Leukemia demonstrate MDCDR's capacity to elucidate pathway-drug associations, offering biologically interpretable guidance for precision treatment.