Advances in Multimodal Deep Learning for Drug Repurposing

Abstract

Computational drug repurposing increasingly integrates chemical, biological, omics, network, text, and clinical data through deep learning. This structured narrative review examines how such modalities are encoded, aligned, and fused. We organize representative studies into four mechanism-centered families: heterogeneous-graph neural networks, multimodal knowledge-graph embeddings, pretrained language/sequence model-based cross-modal alignment, and multi-view or reconstruction-based fusion. Direct drug–disease association and repurposing studies form the core evidence; drug–target interaction, drug–drug interaction, target-identification, molecular-pretraining, and drug–microbe studies are treated as adjacent methodological evidence. We compare architectures, evaluation settings, failure modes, and evidence levels across oncology, neurology, infectious, and rare diseases. Practical guidance covers leakage-aware random, cold-start, temporal, and cluster-based evaluation; an actionable reproducibility checklist; and a scenario-based model-selection framework. We distinguish computational prioritization, docking, preclinical, retrospective clinical, and prospective evidence, and examine data sparsity, uncertain negatives, missing or noisy modalities, interpretability, and translational limitations. Future priorities include temporal and causal evaluation, external and multi-center validation, federated learning, and emerging therapeutic modalities. Multimodal fusion can improve complementary representation, but its value depends on task definition, data quality, evaluation design, and independent validation.