Deep learning-guided discovery of selective JAK2-JH2 allosteric inhibitors: integration of MLP predictive modeling,...

The JAK2 pseudokinase domain (JH2) is an important therapeutic target in hematologic and oncologic diseases, motivating the search for selective allosteric inhibitors. In this study, a multilayer perceptron (MLP) deep learning model was trained on 1,200 JAK2-targeting compounds and validated internally and externally, while a BREED-based fragment hybridization strategy generated 6,210 new molecules that were screened using MLP scoring, pharmacokinetic filters, and molecular docking. Three compounds-BRD1, BRD2, and BRD3-emerged as promising inhibitors, with BRD1 showing the strongest binding affinity, highest conformational stability, and best selectivity for key JH2 residues, surpassing the reference ligand 36H; MD and ADMET analyses further supported its stability and favorable safety profile. Overall, BRD1 is identified as a strong computational candidate for selective allosteric inhibition of JAK2-JH2, warranting future experimental validation, and all models and code are openly available.