Quantum Simulations of SARS-CoV-2 Main Protease Mpro Enable Accurate Scoring of Diverse Ligands
Yuhang Wang, Sruthi Murlidaran, David A. Pearlman

TL;DR
This paper demonstrates that full density functional quantum mechanical simulations can accurately score diverse ligands binding to SARS-CoV-2 Mpro, enabling rapid, large-scale drug screening with high precision.
Contribution
The study introduces a cloud-native DFT/QM approach for ligand binding energy calculations, improving accuracy and speed over traditional methods for SARS-CoV-2 drug discovery.
Findings
DFT/QM simulations successfully scored diverse ligands for Mpro.
Each simulation took approximately 1 hour, enabling high-throughput screening.
The method shows promise for broad application in drug repurposing efforts.
Abstract
The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. Unfortunately, standard computational screening methods face difficulties in ranking diverse ligands to a receptor due to disparate ligand scaffolds and varying charge states. Here, we describe full density functional quantum mechanical (DFT/QM) simulations of Mpro in complex with various ligands to…
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