Exploration of Novel Chemical Spaces to Discover JAK1 Inhibitors: An Ensemble Docking-Guided Deep Learning Approach

Janus kinase 1 (JAK1) is a key regulator of cytokine signaling and a validated therapeutic target in autoimmune, inflammatory, and oncological disorders. However, existing JAK inhibitors such as Tofacitinib and Ruxolitinib are limited by their narrow pyrrolo-[2,3-d]-pyrimidine scaffold, leading to poor isoform selectivity, JAK3 cross-reactivity, and dose-limiting toxicity. Expanding the chemical space for JAK1 inhibition while achieving higher selectivity therefore represents a critical challenge in drug discovery. To overcome these limitations, we developed a deep learning (DL) based virtual screening framework (VS) that explicitly integrates protein flexibility with a billion-scale chemical exploration. Eight high-resolution JAK1 crystal structures were employed to capture conformational diversity of the ATP-binding pocket. Ensemble docking scores derived from these structures were used to train a deep neural network (DNN) classifier on rigorously curated data sets. The model was applied to over 1.1 billion commercially available compounds from the ZINC database, identifying 131,730 high-confidence candidates. Redocking analysis confirmed that 57% of these compounds consistently surpassed a stringent activity threshold across all receptor conformations, underscoring the robustness of the approach. Scaffold-based analysis of the top 10% candidates revealed 7652 unique chemotypes, with only 13 overlapping with scaffolds of known JAK1 inhibitors, highlighting the substantial novelty of the predicted chemical space. Furthermore, physicochemical and ADME filtering enriched for candidates with favorable drug-like properties. By explicitly embedding receptor flexibility into a scalable artificial intelligence framework, this study establishes a generalizable strategy for kinase-targeted drug discovery and opens new opportunities for selective JAK1 inhibitor development.