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.