Identification of novel PKR inhibitory chemotypes via integrated molecular modelling and machine learning

Interferon-inducible RNA-dependent protein kinase (PKR) is an emerging therapeutic target involved in cancer, neurodegeneration, and inflammatory disorders; however, the discovery of potent and structurally diverse PKR inhibitors remains limited. In this study, we report an integrated computational-experimental strategy for the identification of novel PKR inhibitory chemotypes. Pharmacophore models were generated from flexible docking of structurally diverse reference inhibitors and validated using receiver operating characteristic (ROC) analysis. To better capture ligand flexibility and address data scarcity, multiple conformations of 223 PKR inhibitors were employed as a data augmentation strategy in machine learning-based QSAR modelling. Among several algorithms evaluated, a Naïve Bayes classifier combined with genetic function algorithm (GFA) feature selection provided the most predictive model. The optimized model, incorporating a single pharmacophore hypothesis and key physicochemical descriptors, was applied to virtual screening of the Boehringer Ingelheim opnMe compound library. Experimental validation using a PKR kinase assay identified the pre-synthesized compound BI-8128 as a potent PKR inhibitor, with an IC₅₀ value of 174.8 nM, demonstrating higher potency than the reference inhibitor C16 under identical conditions. Notably, this compound represents a structurally distinct scaffold compared to reported PKR inhibitors, indicating effective scaffold hopping. To the best of our knowledge, this is the first report of PKR inhibitory activity for BI-8128. Overall, this work demonstrates that integrating docking-derived pharmacophores with conformational ensemble-based machine learning provides an effective approach for discovering novel inhibitors against underexplored kinase targets.