Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors. (April 2023) - Lien, Ssu-Ting; Lin, Tony Eight; Hsieh, Jui-Hua; Sung, Tzu-Ying; Chen, Jun-Hong; Hsu, Kai-Cheng
Abstract: Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine learning models have been generated to aid in simplifying and improving the prediction of candidate compounds. Models tuned for predicting kinase inhibitors have been established. However, an effective model can be limited by the size of the chosen training dataset. In this study, we tested several machine learning models to predict potential kinase inhibitors. A dataset was curated from a number of publicly available repositories. This resulted in a comprehensive dataset covering more than half of the human kinome. More than 2, 000 kinase models were established using different model approaches. The performances of the models were compared, and the Keras-MLP model was determined to be the best performing model. The model was then used to screen a chemical library for potential inhibitors targeting platelet-derived growth factor receptor-β (PDGFRB). Several PDGFRB candidates were selected, and in vitro assays confirmed four compounds with PDGFRB inhibitory activity and IC50 values in the nanomolar range. These results show the effectiveness of machine learning models trained on the reported dataset. This report would aid in the establishment of machine learning models as well as in the discovery of novel kinase inhibitors. Graphical abstract: Image 1 Highlights: A dataset containing more than 6, 500, 000 bioactive data was curated. More than 2000 machine learning models targeting the human kinome were generated. Favorable ACC (0.864 ± 0.084) and AUC (0.912 ± 0.073) model averages were obtained. A screening campaign for PDGFR identified four inhibitors with nanomolar potency.