Author: Capponi, Sara; Wang, Shangying; Navarro, Erik J.; Bianco, Simone
Title: AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations Cord-id: wyfd8e9x Document date: 2021_7_7
ID: wyfd8e9x
Snippet: 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
Document: 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.
Search related documents:
Co phrase search for related documents- long simulation and low high potential: 1
Co phrase search for related documents, hyperlinks ordered by date