Integrating Machine Learning and Multi-Omics Data for Novel Drug Target Identification

Abstract

Identifying novel drug targets is central to accelerating therapeutic discovery and precision medicine. The proliferation of high-throughput “omics” technologies (genomics, transcriptomic, proteomics, metabolomics, epigenetics, single-cell omics, etc.) has created unprecedented opportunities for holistic molecular characterization of disease states. When combined with advances in machine learning (ML)   including classical statistical learning, ensemble methods, representation learning, and graph-based deep learning   multi-omics integration enables systems-level discovery of candidate targets that would be missed by single-modality analyses. This article provides a comprehensive, scholarly synthesis of current methodologies for integrating multi-omics data with ML for drug target identification. We: (1) review types of omics data and pre-processing requirements; (2) compare integration strategies (early, intermediate, late) and representative algorithms; (3) discuss ML models commonly used, from penalized regressions to graph neural networks and explainable AI (XAI) approaches; (4) present evaluation metrics and validation strategies (computational, in vitro, in vivo); (5) examine case studies and translational successes; and (6) analyze major challenges   data heterogeneity, batch effects, small-n large-p regimes, interpretability, and regulatory considerations   with pragmatic recommendations. We close by outlining future directions, including federated learning, hybrid experimental–computational pipelines, and clinical translation pathways. The review is intended for computational biologists, translational scientists, and pharmaceutical researchers aiming to apply rigorous ML-enabled, multi-omics pipelines for robust target discovery.