ArticleActa pharmacologica Sinica2026
AI and experimental convergence: a synergistic pathway to JAK2 inhibitor discovery.
Article in Acta pharmacologica Sinica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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