Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders.

ABSTRACT: Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro-ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training se

SUBMITTER: Varghese A 

PROVIDER: S-EPMC12300542 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature