Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2,Chemical Reviews

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Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Chemical Reviews ( IF 51.4 ) Pub Date : 2022-05-20 , DOI: 10.1021/acs.chemrev.1c00965
Kaifu Gao ₁ , Rui Wang ₁ , Jiahui Chen ₁ , Limei Cheng ₂ , Jaclyn Frishcosy ₁ , Yuta Huzumi ₁ , Yuchi Qiu ₁ , Tom Schluckbier ₁ , Xiaoqi Wei ₁ , Guo-Wei Wei _{1,

3,

4}

Affiliation

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein–protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

中文翻译：

以方法论为中心的 SARS-CoV-2 分子建模、模拟和预测综述

尽管过去两年付出了巨大的努力，但我们对严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2)、病毒与宿主相互作用、免疫反应、毒力、传播和进化的了解仍然非常有限。这种限制需要进一步深入调查。计算研究因其成本低、效率高且不受安全和道德约束而成为对抗 2019 年冠状病毒病 (COVID-19) 不可或缺的组成部分。此外，控制 SARS-CoV-2 全球进化和传播的机制无法通过个体实验来揭示，而是通过整合大量病毒序列的基因分型、蛋白质-蛋白质相互作用的生物物理模型、深度突变数据、深度学习和高等数学。关于 SARS-CoV-2 的分子建模、模拟和预测以及药物、疫苗、抗体和诊断学的相关开发的文献有海量。为了向读者提供有关该文献的快速更新，我们提出了全面且系统的以方法论为中心的综述。讨论了分子生物物理学、生物信息学、化学信息学、机器学习和数学等方面。这篇综述对于正在寻找为 SARS-CoV-2 研究做出贡献的研究人员以及对该领域现状感兴趣的研究人员将有益。

更新日期：2022-05-20

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