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Predicting global diet-disease relationships at the atomic level: a COVID-19 case study
Over the past few months, numerous studies harnessed in silico methods such as molecular docking to evaluate food compounds for inhibitory activity against coronavirus infection and replication. These studies capitalize on the efficiency of computational methods to quickly guide subsequent research...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721929/ https://www.ncbi.nlm.nih.gov/pubmed/35004187 http://dx.doi.org/10.1016/j.cofs.2021.12.013 |
Sumario: | Over the past few months, numerous studies harnessed in silico methods such as molecular docking to evaluate food compounds for inhibitory activity against coronavirus infection and replication. These studies capitalize on the efficiency of computational methods to quickly guide subsequent research and examine diet-disease relationships, and their sudden widespread utility may signal new opportunities for future antiviral and bioactive food research. Using Coronavirus Disease 2019 (COVID-19) research as a case study, we herein provide an overview of findings from studies using molecular docking to study food compounds as potential inhibitors of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), explore considerations for the critical interpretation of study findings, and discuss how these studies help shape larger conversations of diet and disease. |
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