Structure-Based QSAR Modeling of RET Kinase Inhibitors from 49 Different 5,6-Fused Bicyclic Heteroaromatic Cores to Patent-Driven Validation

Abstract

RET receptor tyrosine kinase is crucial for nerve and tissue development but can be an important oncogenic driver. This study focuses on exploring the design principles of potent RET inhibitors through molecular docking and 3D-QSAR modeling of 5,6-fused bicyclic heteroaromatic derivatives. First of all, RET inhibitors of 49 different bicyclic substructures were collected from five different data sources and selected through molecular docking simulations. QSAR models were built from the 3399 conformers of 952 RET inhibitors using the partial least-squares method and statistically evaluated. The optimal QSAR model exhibited high predictive performance, with R² (of training data) and Q² (of test data) values of 0.801 and 0.794, respectively, effectively predicting known inhibitors. The optimal model was doubly verified by patent-filed RET inhibitors as the out-of-set data to demonstrate acceptable residual analysis results. Moreover, feature importance analysis of the QSAR model outlined the impact of substituent characteristics on the inhibitory activity within the 5,6-fused bicyclic heteroaromatic core structures. Furthermore, the relationship between structure and inhibitory activity was successfully applied to the RET screening of known clinical and nonclinical kinase inhibitors to afford accurate off-target prediction.