Matches in SemOpenAlex for { <https://semopenalex.org/work/W3202379675> ?p ?o ?g. }
- W3202379675 abstract "We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2." @default.
- W3202379675 created "2021-10-11" @default.
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- W3202379675 date "2021-10-01" @default.
- W3202379675 modified "2023-09-26" @default.
- W3202379675 title "AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations" @default.
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- W3202379675 doi "https://doi.org/10.1140/epje/s10189-021-00119-5" @default.
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