Machine learning-guided identification and simulation-based validation of potent JAK3 inhibitors for cancer therapy.

ABSTRACT: Janus kinase 3 (JAK3) is a hematopoietic-specific kinase implicated in cytokine signaling and immune dysregulation and has recently been associated with cancer progression. However, selective and potent JAK3 inhibitors remain underdeveloped. In this study, we established a machine learning (ML)-based pipeline to identify novel JAK3 inhibitors with anti-cancer potential. A curated ChEMBL dataset of JAK3 inhibitors was used to train multiple ML classifiers, with the Random Forest model achieving the highest performance (AUC = 0.80, F1-score = 0.92). This model was applied to virtually screen 25,084 ChEMBL compounds, yielding 400 high-confidence candidates (prediction score > 0.9). Docking analysis identified ten top binders (binding affinity ≤ -8.5 kcal/mol), of which three CHEMBL49087, CHEM

SUBMITTER: Wei H 

PROVIDER: S-EPMC12700429 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature