Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor

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Detalles Bibliográficos
Publicado en:	Nature Communications vol. 16, no. 1 (2025), p. 6949-6961
Autor principal:	Elez, Katarina
Otros Autores:	Hempel, Tim, Shrimp, Jonathan H., Moor, Nicole, Raich, Lluís, Rocha, Cheila, Winter, Robin, Le, Tuan, Pöhlmann, Stefan, Hoffmann, Markus, Hall, Matthew D., Noé, Frank
Publicado:	Nature Publishing Group
Materias:	Coronaviruses Simulation Datasets Learning Inhibitors Computational efficiency Effectiveness Computing costs Drug development Severe acute respiratory syndrome coronavirus 2 Computer applications Drug screening Viral diseases Drugs Libraries Molecular dynamics Candidates COVID-19 Proteins Therapeutic applications Environmental
Acceso en línea:	Citation/Abstract Full Text Full Text - PDF
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Descripción
Resumen:	Drug screening resembles finding a needle in a haystack: identifying a few effective inhibitors from a large pool of potential drugs. Large experimental screens are expensive and time-consuming, while virtual screening trades off computational efficiency and experimental correlation. Here we develop a framework that combines molecular dynamics (MD) simulations with active learning. Two components drastically reduce the number of candidates needing experimental testing to less than 20: (1) a target-specific score that evaluates target inhibition and (2) extensive MD simulations to generate a receptor ensemble. The active learning approach reduces the number of compounds requiring experimental testing to less than 10 and cuts computational costs by ∼29-fold. Using this framework, we discovered BMS-262084 as a potent inhibitor of TMPRSS2 (IC50 = 1.82 nM). Cell-based experiments confirmed BMS-262084’s efficacy in blocking entry of various SARS-CoV-2 variants and other coronaviruses. The identified inhibitor holds promise for treating viral and other diseases involving TMPRSS2.Approaches making virtual and experimental screening more resource-efficient are vital for identifying effective inhibitors from a vast pool of potential drugs but remain elusive. Here, the authors address this issue by developing an active learning framework leveraging high-throughput molecular dynamics simulations to identify potential inhibitors for therapeutic applications.
ISSN:	2041-1723
DOI:	10.1038/s41467-025-62139-5
Fuente:	Health & Medical Collection