Advances in multimodal data integration for drug efficacy prediction: Methodological evolution and clinical translation.

Abstract

Drug efficacy prediction remains a cornerstone of drug development and precision therapy. However, integrating heterogeneous biomedical data, including multi-omics profiles, pathological imaging, electronic health records, and pharmacokinetic-pharmacodynamic (PK/PD) time-series, faces three fundamental barriers, namely cross-domain distribution shifts between preclinical and clinical data, relational mismatches between isolated vector representations and biological networks, and feature heterogeneity across disparate modalities. To address these challenges, three AI paradigms have emerged, transfer learning for cross-domain alignment, graph neural networks for structured relational modeling, and Transformers for global cross-modal feature interaction. Importantly, these techniques form a many-to-many complementary system rather than a one-to-one correspondence, a key insight that this review explicitly formalizes. We further elaborate encoding workflows for PK/PD data to bridge static molecular signatures with dynamic in vivo exposure trajectories. Four graded clinical applications are outlined, including personalized monotherapy, combination optimization, drug repurposing, and preclinical-to-clinical evaluation of novel candidates. We also dissect persistent bottlenecks such as data harmonization, model interpretability, and prospective validation, and propose five actionable directions, namely privacy-preserving benchmarks, causally interpretable models, temporal dynamic frameworks, cross-domain generalization, and lightweight clinical tools. By integrating theoretical rationales, methodological synergies, and hierarchical translational scenarios, this review provides a unified roadmap to accelerate the clinical deployment of multimodal drug response prediction.