March 3, 2026Open Access

AI and experimental convergence: a synergistic pathway to JAK2 inhibitor discovery

Key Points

The study identifies JAK2 inhibitors with IC50 values below 10 μM, showing high potential for treating inflammatory diseases and cancers.
The CatBoost model combined with Morgan fingerprints achieved an impressive accuracy of 0.94 during testing, indicating significant predictive power.
Computational analyses such as molecular docking and dynamics simulations were utilized to evaluate the top JAK2 inhibitor candidates.
The approach integrates AI-driven modeling with experimental tests, suggesting a transformative strategy for developing targeted therapies.

Abstract

Janus kinase 2 (JAK2) is an important therapeutic target for various inflammatory diseases, cancers, and rheumatoid arthritis. Therefore, inhibiting JAK2 has become a promising approach for treating these conditions. In this study, molecular descriptors such as Morgan fingerprints, Molecular Access System (MACCS), and PaDEL were calculated and used to develop machine-learning models. Among these models, CatBoost combined with Morgan fingerprints performed the best, achieving an accuracy of 0.94 on the test dataset. This CatBoost model was then used to screen the Korean Chemical Databank (KCB) to identify the most potent JAK2 inhibitors. Computational analyses, including density functional theory (DFT), molecular docking, and molecular dynamics simulations, were carried out to evaluate the performance of the top-ranked molecules. Finally, four compounds were selected for experimental testing, and the results showed that their IC50 values were less than 10 μM. The integration of AI-driven modeling with experimental validation provides a promising strategy for personalized medicine, enabling the development of more precise and effective kinase-targeted therapies while reducing the time and cost required to bring new drugs to clinical trials.

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