Abstract Precision medicine is transforming drug discovery from empirical, population-based approaches toward data-driven, mechanistically informed strategies tailored to individual molecular profiles. Central to this transformation is multi-omics integration—the systematic analysis of genomic, transcriptomic, proteomic, metabolomic, and epigenomic data—which enables comprehensive characterization of disease mechanisms, therapeutic vulnerabilities, and inter- and intra-patient (single-cell) heterogeneity. By moving beyond reductionist, single-layer analyses, multi-omics captures emergent properties of biological systems, revealing causal relationships between molecular variation and clinical phenotypes that are essential for robust target discovery, validation, and lead optimization. This mini-review examines how precision medicine and multi-omics are reshaping the drug discovery pipeline, emphasizing the critical roles of artificial intelligence (AI), FAIR data principles (Findable, Accessible, Interoperable, Reusable), and governance frameworks. We highlight advances in network-based integration, multi-view machine learning, and AI-driven target prioritization, demonstrating how these approaches accelerate hypothesis generation while maintaining reproducibility and traceability. Real-world applications—from HER2-targeted therapies in breast cancer to PARP inhibitors for BRCA-mutated tumors—illustrate the clinical impact of multi-omics-guided drug development. Emerging technologies, including single-cell and spatially resolved multi-omics, promise unprecedented resolution for dissecting tissue heterogeneity, microenvironmental context, and therapeutic resistance mechanisms. Integration of these modalities with foundation models and knowledge graphs comprised of FAIR data will enable cross-modal reasoning, predictive modeling, and patient stratification at scale. However, persistent challenges—data heterogeneity, computational complexity, ethical considerations, and regulatory frameworks—require coordinated solutions. By synthesizing conceptual advances, practical applications, and emerging challenges, we articulate a vision for FAIR-enabled, AI-driven precision medicine as the foundation for next-generation therapeutic discovery.
Judge et al. (Thu,) studied this question.