Discovery Of Novel Kinase Inhibitors Research Articles

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors

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.

Structure-based Virtual Screening Approaches in Kinase-directed Drug Discovery.

Protein kinases are one of the most targeted protein families in current drug discovery pipelines. They are implicated in many oncological, inflammatory, CNS-related and other clinical indications. Virtual screening is a computational technique with a diverse set of available tools that has been shown many times to provide novel starting points for kinase-directed drug discovery. This review starts with a concise overview of the function, structural features and inhibitory mechanisms of protein kinases. In addition to briefly reviewing the practical aspects of structure-based virtual screenings, we discuss several case studies to illustrate the state of the art in the virtual screening for type I, type II, allosteric (type III-V) and covalent (type VI) kinase inhibitors. With this review, we strive to provide a summary of the latest advances in the structure-based discovery of novel kinase inhibitors, as well as a practical tool to anyone who wishes to embark on such an endeavor.

Advanced molecular dynamics simulation methods for kinase drug discovery.

Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.