Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

ABSTRACT: To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turn

SUBMITTER: Nguyen TH 

PROVIDER: S-EPMC9950021 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature