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• W3089341832 abstract "Significance The COVID-19 caused by SARS-CoV-2 virus has posed a tremendous threat to human health. The interactions between human angiotensin-converting enzyme 2 and the spike glycoprotein of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines. However, the simulation of these interactions in fluctuating surroundings is challenging because it requires many electronic structure calculations at the quantum mechanics level for a large number of representative configurations. We report a machine learning protocol that can efficiently predict the IR spectra of SARS-CoV-2 with high efficiency and characterize fine changes in IR spectra associated with variations of protein secondary structures. Machine learning provides a cost-effective tool for monitoring of real-time interactions between the SARS-CoV-2 and human ACE2." @default.
• W3089341832 created "2020-10-08" @default.
• W3089341832 creator A5010193064 @default.
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• W3089341832 date "2021-06-14" @default.
• W3089341832 modified "2023-10-02" @default.
• W3089341832 title "AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2" @default.
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• W3089341832 doi "https://doi.org/10.1073/pnas.2025879118" @default.
• W3089341832 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8256048" @default.
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• W3089341832 hasPublicationYear "2021" @default.
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