Intelligent Drug Repurposing: Integrating Multi-Omics Data, Machine Learning and Clinical Evidence

Jeffrey Volenec

Intelligent Drug Repurposing: Integrating Multi-Omics Data, Machine Learning and Clinical Evidence

Author(s)	Jeffrey Volenec
Country	United States
Abstract	Drug repurposing has emerged as an important strategy for identifying new therapeutic applications for existing drugs, potentially reducing the time, cost and risks associated with conventional drug development. However, traditional repurposing approaches frequently rely on individual data sources and may fail to capture the complex molecular mechanisms underlying disease and treatment response. The integration of multi-omics data, machine learning and clinical evidence offers a more comprehensive framework for intelligent drug repurposing. This paper examines how genomic, transcriptomic, proteomic, metabolomic and epigenomic information can be combined with machine-learning models and real-world clinical evidence to identify, prioritise and validate new drug–disease associations. A conceptual framework is proposed that integrates molecular signatures, drug–target relationships, biological pathways, electronic health records, clinical trials and pharmacovigilance data. Particular attention is given to knowledge graphs, graph neural networks, representation learning, causal inference, network medicine and multimodal learning. Applications across oncology, infectious diseases, neurological disorders, rare diseases and chronic conditions are discussed. The paper also examines major challenges, including heterogeneous data quality, missing information, population bias, confounding, interpretability, data privacy and the distinction between statistical association and causal therapeutic benefit. The analysis suggests that intelligent drug repurposing should be implemented as a staged discovery pipeline in which computational predictions are progressively evaluated through molecular evidence, retrospective clinical analyses, prospective trials and pharmacovigilance. The convergence of multi-omics, artificial intelligence and clinical evidence could transform drug repurposing from largely hypothesis-driven screening into a data-integrated and continuously learning therapeutic discovery process.
Keywords	Drug Repurposing, Multi-Omics, Machine Learning, Clinical Evidence, Precision Medicine, Network Medicine, Drug–Disease Associations, Pharmacogenomics, Artificial Intelligence, Therapeutic Discovery.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-27

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Drug Repurposing: Integrating Multi-Omics Data, Machine Learning and Clinical Evidence

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