/pdf/shu-ping-computational-homology-1o0kbmii0p.pdf

書評 Computational Homology

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020 In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment;clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures;and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics ©2020 AI Access Foundation All rights reserved

/pdf/mapping-the-landscape-of-artificial-intelligence-254vzkucyl.pdf

Mapping the landscape of artificial intelligence applications against COVID-19

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 2.5 million confirmed cases as of April 23, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, clinical, and societal applications. We also review datasets, tools, and resources needed to facilitate AI research. Finally, we discuss strategic considerations related to the operational implementation of projects, multidisciplinary partnerships, and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

/pdf/mapping-the-landscape-of-artificial-intelligence-4q63rreozk.pdf

Mapping the Landscape of Artificial Intelligence Applications against COVID-19

The novel coronavirus whose outbreak took place in December 2019 continues to spread at a rapid rate worldwide. In the absence of an effective vaccine, inhibitor repurposing or de novo drug design may offer a longer-term strategy to combat this and future infections due to similar viruses. Here, we report on detailed classical and mixed-solvent molecular dynamics simulations of the main protease (Mpro) enriched by evolutionary and stability analysis of the protein. The results were compared with those for a highly similar severe acute respiratory syndrome (SARS) Mpro protein. In spite of a high level of sequence similarity, the active sites in both proteins showed major differences in both shape and size, indicating that repurposing SARS drugs for COVID-19 may be futile. Furthermore, analysis of the binding site's conformational changes during the simulation time indicated its flexibility and plasticity, which dashes hopes for rapid and reliable drug design. Conversely, structural stability of the protein with respect to flexible loop mutations indicated that the virus' mutability will pose a further challenge to the rational design of small-molecule inhibitors. However, few residues contribute significantly to the protein stability and thus can be considered as key anchoring residues for Mpro inhibitor design.

https://www.mdpi.com/1422-0067/21/9/3099/pdf

Structural and Evolutionary Analysis Indicate That the SARS-CoV-2 Mpro Is a Challenging Target for Small-Molecule Inhibitor Design.

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

/pdf/artificial-intelligence-in-the-battle-against-coronavirus-3ayvkhps4h.pdf

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

The World Health Organization (WHO) has declared the 2019 novel coronavirus (2019-nCoV) infection outbreak a global health emergency. Currently, there is no effective anti-2019-nCoV medication. The sequence identity of the 3CL proteases of 2019-nCoV and SARS is 96%, which provides a sound foundation for structural-based drug repositioning (SBDR). Based on a SARS 3CL protease X-ray crystal structure, we construct a 3D homology structure of 2019-nCoV 3CL protease. Based on this structure and existing experimental datasets for SARS 3CL protease inhibitors, we develop an SBDR model based on machine learning and mathematics to screen 1465 drugs in the DrugBank that have been approved by the U.S. Food and Drug Administration (FDA). We found that many FDA approved drugs are potentially highly potent to 2019-nCoV.

/pdf/potentially-highly-potent-drugs-for-2019-ncov-3uatuw754u.pdf

Potentially highly potent drugs for 2019-nCoV

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Potentially highly potent drugs for 2019-nCoV