Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations

Abstract

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there are no approved RIPK1 inhibitors available. In this study, four machine learning algorithms were employed (random forest, extra trees, extreme gradient boosting and light gradient boosting machine) to predict small molecule inhibitors of RIPK1. The statistical metrics revealed similar performance and demonstrated outstanding predictive capabilities in all four models. Molecular docking and clustering analysis were employed to confirm six compounds that are structurally distinct from existing RIPK1 inhibitors. Subsequent molecular dynamics simulations were performed to evaluate the binding ability of these compounds. Utilizing the Shapley additive explanation (SHAP) method, the 1855 bit has been identified as the most significant molecular fingerprint fragment. The findings propose that these six small molecules exhibit promising potential for targeting RIPK1 in associated diseases. Notably, the identification of Cpd-1 small molecule (ZINC000085897746) from the Musa acuminate highlights its natural product origin, warranting further attention and investigation.