Genomics-Driven Drug Discovery: Functional, Computational, and Integrative Approaches

The convergence of genomics, systems biology, and artificial intelligence is transforming the landscape of drug discovery. Modern genomic technologies now extend far beyond identifying candidate genes: they enable mechanistic interpretation of disease pathways and networks, target validation, personalization or patient stratification, and systematic drug repurposing.

High-throughput functional genomics, including CRISPR-based perturbation screening, single-cell profiling, and phenotypic readouts, coupled with longitudinal systems data provides unprecedented resolution for dissecting causal mechanisms.

Large-scale human genetic resources, multi-omics datasets, and computational advances are enabling predictive and integrative strategies. Integrating genetic evidence with machine learning, network-level analyses, and systems-based modeling promises to enhance success rates and precision in therapeutic development, particularly by identifying mechanistically anchored and genetically supported targets. This Research Topic brings together interdisciplinary work spanning functional genomics, computational modeling, translational genetics, and AI-driven discovery pipelines. We welcome original research, methods, reviews, and perspectives that advance data-driven therapeutic innovation.

We encourage submissions covering (but not limited to) the following areas:

- Functional Genomics for Target Discovery: CRISPR/Cas9 and other perturbation technologies; genome-wide loss- and gain-of-function screens; single-cell functional genomics; high-content phenotypic profiling to identify and validate druggable targets.

- Human Genetics and Therapeutic Validation: Use of GWAS, fine-mapping, rare variant analysis, and polygenic frameworks to prioritize causal genes, pathways, and tissues; genetic evidence linking targets to clinical outcomes.

- Multi-Omics Integration for Mechanistic Insights: Integrative analysis of genomics, transcriptomics, proteomics, epigenomics, metabolomics, and spatial omics to define disease mechanisms and actionable biological pathways.

- AI, Machine Learning, and Predictive Modeling: Application of generative AI, graph and network models, machine learning for target identification, drug–target interaction prediction, and large-language-model–based hypothesis generation or knowledge synthesis.

- Drug Repurposing and Precision Medicine: Genomic and transcriptomic signatures for drug repositioning; data-driven matching of existing therapeutics to molecular subtypes or genetically defined patient groups.

- Mechanistic Translation of Genetic Findings: Linking genetic signals to biological function through pathway modeling, perturbation assays, cellular and in vivo validation studies, and approaches that bridge genetic association to therapeutic development.

All article types other than Case Reports may be considered for this Research Topic.