Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor
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| Publicado en: | Nature Communications vol. 16, no. 1 (2025), p. 6949-6961 |
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| Otros Autores: | , , , , , , , , , , |
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Nature Publishing Group
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2041-1723 | ||
| 024 | 7 | |a 10.1038/s41467-025-62139-5 |2 doi | |
| 035 | |a 3234542037 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 145839 |2 nlm | ||
| 100 | 1 | |a Elez, Katarina |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786) | |
| 245 | 1 | |a Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Coronaviruses | ||
| 653 | |a Simulation | ||
| 653 | |a Datasets | ||
| 653 | |a Learning | ||
| 653 | |a Inhibitors | ||
| 653 | |a Computational efficiency | ||
| 653 | |a Effectiveness | ||
| 653 | |a Computing costs | ||
| 653 | |a Drug development | ||
| 653 | |a Severe acute respiratory syndrome coronavirus 2 | ||
| 653 | |a Computer applications | ||
| 653 | |a Drug screening | ||
| 653 | |a Viral diseases | ||
| 653 | |a Drugs | ||
| 653 | |a Libraries | ||
| 653 | |a Molecular dynamics | ||
| 653 | |a Candidates | ||
| 653 | |a COVID-19 | ||
| 653 | |a Proteins | ||
| 653 | |a Therapeutic applications | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Hempel, Tim |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Physics, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Microsoft Research AI for Science, Berlin, Germany (ROR: https://ror.org/04bpb0r34) (GRID: grid.506102.0) | |
| 700 | 1 | |a Shrimp, Jonathan H. |u National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA (ROR: https://ror.org/01cwqze88) (GRID: grid.94365.3d) (ISNI: 0000 0001 2297 5165) | |
| 700 | 1 | |a Moor, Nicole |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) | |
| 700 | 1 | |a Raich, Lluís |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786) | |
| 700 | 1 | |a Rocha, Cheila |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) | |
| 700 | 1 | |a Winter, Robin |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Bioinformatics, Bayer AG, Berlin, Germany (ROR: https://ror.org/04hmn8g73) (GRID: grid.420044.6) (ISNI: 0000 0004 0374 4101) | |
| 700 | 1 | |a Le, Tuan |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Bioinformatics, Bayer AG, Berlin, Germany (ROR: https://ror.org/04hmn8g73) (GRID: grid.420044.6) (ISNI: 0000 0004 0374 4101) | |
| 700 | 1 | |a Pöhlmann, Stefan |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) | |
| 700 | 1 | |a Hoffmann, Markus |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) | |
| 700 | 1 | |a Hall, Matthew D. |u National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA (ROR: https://ror.org/01cwqze88) (GRID: grid.94365.3d) (ISNI: 0000 0001 2297 5165) | |
| 700 | 1 | |a Noé, Frank |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Physics, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Microsoft Research AI for Science, Berlin, Germany (ROR: https://ror.org/04bpb0r34) (GRID: grid.506102.0); Department of Chemistry, Rice University, Houston, TX, USA (ROR: https://ror.org/008zs3103) (GRID: grid.21940.3e) (ISNI: 0000 0004 1936 8278) | |
| 773 | 0 | |t Nature Communications |g vol. 16, no. 1 (2025), p. 6949-6961 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
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