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 IC(50) 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.