Genome-scale association studies and functional screens routinely implicate hundreds of candidate genes per disease, yet only a few will be clinically validated as drug targets. Choosing which to pursue is a central drug-discovery decision that depends on interpreting each candidate in its biological context. Curated pathway databases provide this context, while enrichment analysis applies it at scale, turning gene-level signals from genome-wide association, transcriptomic, proteomic and CRISPR studies into mechanistic hypotheses for prioritisation. This review examines how pathway-based methods inform target prioritisation, the databases and tools available for this purpose, and why pathway co-membership should be viewed as a starting point for validation rather than as evidence of causal involvement.
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Rusina et al. (2026) studied this question.
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