Next-Generation Artificial Intelligence for Drug Repurposing: Integrating Machine Learning, Deep Learning, Multi-Omics, Knowledge Graphs, and Clinical Translation: A Review

Abstract

Drug repurposing has emerged as a promising strategy to accelerate drug discovery by identifying new therapeutic indications for existing approved or investigational drugs, thereby reducing development time, cost, and clinical risk compared with traditional de novo drug development. The rapid expansion of biomedical big data, together with advances in artificial intelligence (AI) and machine learning (ML), has transformed computational drug repurposing into a data-driven and highly efficient discipline. Conventional machine learning algorithms, including Support Vector Machines, Random Forests, and gradient boosting methods, have demonstrated significant utility in predicting drug-target and drug-disease associations. More recently, deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformer-based models, and Graph Neural Networks (GNNs) have enabled the integration of heterogeneous datasets, including chemical structures, transcriptomics, proteomics, metabolomics, pharmacogenomics, protein-protein interaction networks, and electronic health records, substantially improving prediction accuracy. This review provides a comprehensive overview of AI-driven drug repurposing, covering computational strategies, publicly available biomedical databases, feature representation methods, machine learning and deep learning algorithms, and their applications in cancer, infectious diseases, neurological disorders, cardiovascular diseases, and rare diseases. Furthermore, recent advances in knowledge graphs, explainable artificial intelligence (XAI), federated learning, foundation models, and large language models (LLMs) are discussed as emerging technologies capable of improving prediction reliability, interpretability, and clinical applicability. Current challenges, including data heterogeneity, limited external validation, algorithmic bias, model interpretability, and regulatory barriers, are critically evaluated. Finally, future perspectives focusing on multimodal multi-omics integration, digital twins, real-world evidence, and precision medicine are presented.