ClinOracle: Hierarchical AI Prediction of Target Binding and Patient-Derived Functional Activity Across Diverse Therapeutic Targets

Abstract

AI platforms for drug discovery routinely achieve high hit rates against biochemical targets, yet the central translational challenge remains predicting whether a compound will be functionally active in patient-derived human cells. Here, we present ClinOracle, a hierarchical graph neural network that jointly predicts target binding and patient-derived functional activity by modeling functional activity as conditional on target engagement. Applied across five therapeutic targets spanning oncology, autoimmune, and neuroinflammatory diseases, ClinOracle ranked candidates using a Priority Score integrating translational probability with a developability score based on ADME and drug-likeness, advancing prioritized compounds through multistage prospective validation from biophysical binding to in vivo efficacy. Compounds with the highest Priority Scores consistently outperformed lower-ranked candidates across prospective experimental validation, demonstrating that hierarchical AI can prioritize compounds with patient-derived functional activity directly from molecular structure, rather than biochemical activity alone.