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Nat Commun
. 2025 Jul 29;16(1):6949.
doi: 10.1038/s41467-025-62139-5. Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor
Katarina Elez 1 , Tim Hempel 1 2 3 , Jonathan H Shrimp 4 , Nicole Moor 5 6 , Lluís Raich 1 , Cheila Rocha 5 6 , Robin Winter 1 7 , Tuan Le 1 7 , Stefan Pöhlmann 5 6 , Markus Hoffmann 5 6 , Matthew D Hall 4 , Frank Noé 8 9 10 11
Affiliations
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.
. 2025 Jul 29;16(1):6949.
doi: 10.1038/s41467-025-62139-5. Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor
Katarina Elez 1 , Tim Hempel 1 2 3 , Jonathan H Shrimp 4 , Nicole Moor 5 6 , Lluís Raich 1 , Cheila Rocha 5 6 , Robin Winter 1 7 , Tuan Le 1 7 , Stefan Pöhlmann 5 6 , Markus Hoffmann 5 6 , Matthew D Hall 4 , Frank Noé 8 9 10 11
Affiliations
- PMID: 40730765
- PMCID: PMC12307812
- DOI: 10.1038/s41467-025-62139-5
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.