Structure-Based Identification of JAK1-Selective Candidates Using Ensemble Docking and Interaction Analysis

Background/Objectives: Selective inhibition of JAK1 remains a major challenge in cytokine-signaling therapeutics due to the high structural similarity of the JAK family. Here, we present an integrated computational framework that combines large-scale binding-site conformational analysis, ensemble docking, and protein–ligand interaction fingerprinting (PLIF) to elucidate the structural determinants of JAK1 selectivity and prioritize JAK1-biased scaffolds. Methods: A curated set of JAK1 and JAK2 catalytic-domain structures was clustered to capture binding-site diversity, and representative conformers were evaluated using >2300 annotated ligands. Docking performance was assessed via AUC, early enrichment metrics, and structural pose validation against experimentally resolved complexes. The workflow was subsequently applied to a library of ~6000 drug-like compounds to prioritize candidates with predicted JAK1 preference. Results: Across the ensemble, the most predictive features reliably separated active from inactive ligands (AUC = 0.78–0.82) and captured subtle, systematic rank shifts supporting the reported JAK1 bias. Interaction fingerprint analysis revealed a conserved hinge-binding motif required for potency, alongside a JAK1-enriched hotspot adjacent to Glu aD.55 that contributes to isoform discrimination. Applied to a library of ~6000 drug-like molecules, the workflow yielded 174 candidates predicted to exhibit preferential JAK1 recognition and reduced JAK2 engagement. Conclusions: These findings define the structural and physicochemical features underlying JAK1 selectivity and illustrate how ensemble-based modeling can guide the discovery of next-generation selective kinase inhibitors.