Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro.

ABSTRACT: Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported

SUBMITTER: Ruatta SM 

PROVIDER: S-EPMC10323144 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature