Identification of lead inhibitors for 3CLpro of SARS-CoV-2 target using machine learning based virtual screening, ADMET analysis, molecular docking and molecular dynamics simulations.

ABSTRACT: The SARS-CoV-2 3CLpro is a critical target for COVID-19 therapeutics due to its role in viral replication. We employed a screening pipeline to identify novel inhibitors by combining machine learning classification with similarity checks of approved medications. A voting classifier, integrating three machine learning classifiers, was used to filter a large database (∼10 million compounds) for potential inhibitors. This ensemble-based machine learning technique enhances overall performance and robustness compared to individual classifiers. From the screening, three compounds M1, M2 and M3 were selected for further analysis. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis compared these candidates to nirmatrelvir and azvudine. Molecular docking followed by 200 n

SUBMITTER: Chhetri SP 

PROVIDER: S-EPMC11408992 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature