RANKOR: Direct Drug Prioritization from Bulk and Single-Cell Transcriptomic Signatures

BackgroundPrioritizing therapeutics from transcriptomic data remains a key challenge in precision medicine. Signature reversal approaches, most commonly implemented through Gene Set Enrichment Analysis (GSEA), have been widely used to match disease signatures to candidate drugs. However, enrichment-based methods can be sensitive to noise and are restricted to previously profiled compounds MethodsWe developed RANKOR, a machine-learning framework designed to rank candidate drugs directly from transcriptomic signatures. Rather than predicting full expression profiles, RANKOR learns structured latent representations of transcriptional responses alongside chemical structure, enabling prioritization from standardized signatures derived from disease states or treatment perturbations. The framework is applicable to both bulk and single-cell transcriptomic data. ResultsAcross large-scale perturbational datasets, RANKOR achieved consistently lower median ranks than similarity- and distance-based approaches, while showing performance comparable to, and in some settings improved over, GSEA. The model generalized across unseen cell types and retained performance in single-cell settings, where it provided more consistent prioritization than existing approaches, such as ASGARD. RANKOR further enabled prioritization of transcriptionally unseen compounds through chemical-space embedding and achieved substantially reduced computation times. Robustness analyses demonstrated stable performance under moderate noise and degradation under extreme perturbation or gene shuffling. Gene attribution analyses indicated that prioritization decisions are driven by coherent and mechanism-relevant transcriptional programs. ConclusionsRANKOR provides a scalable framework for transcriptomics-guided drug prioritization that can complement and extend existing approaches, such as GSEA. It can also support therapeutic hypothesis generation from bulk and single-cell data while leveraging the generalisability and computational efficiency of machine learning models.