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Matches in SemOpenAlex for { <https://semopenalex.org/work/W3149898012> ?p ?o ?g. }

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• W3149898012 startingPage "2075" @default.
• W3149898012 abstract "Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low-throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high-throughput supervised molecular dynamics simulations (HT-SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS-CoV-2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD-based posing approaches, a high number of incorrect binding modes still complicate HT-SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in-silico pipeline, allowing us to prioritize only the more reliable predictions." @default.
• W3149898012 created "2021-04-13" @default.
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• W3149898012 date "2021-05-06" @default.
• W3149898012 modified "2023-10-03" @default.
• W3149898012 title "Inspecting the Mechanism of Fragment Hits Binding on SARS‐CoV‐2 M ^pro by Using Supervised Molecular Dynamics (SuMD) Simulations" @default.
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• W3149898012 doi "https://doi.org/10.1002/cmdc.202100156" @default.
• W3149898012 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8250706" @default.
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• W3149898012 hasPublicationYear "2021" @default.
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