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

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Mapping the landscape of artificial intelligence applications against COVID-19

Objective Science and technology sector constituting of data science, machine learning and artificial intelligence are contributing towards COVID-19. The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines. Methods A progressive search of the database related to modern technology towards COVID-19 is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic. Results We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. Though different studies relating to modern technology towards COVID-19 have come up, yet there are still constrained applications and contributions of technology in this fight. Conclusions On-going progress in the modern technology has contributed in improving people’s lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.

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A review of modern technologies for tackling COVID-19 pandemic.

Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infecte...

Peptide-like and small-molecule inhibitors against Covid-19.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools—including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization—that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets.

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Leveraging Data Science to Combat COVID-19: A Comprehensive Review

The emergence of the 2019 novel coronavirus (2019-nCoV), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches One of the most important 2019-nCoV protein targets is the 3C-like protease for which the crystal structure is known Most of the immediate efforts are focused on drug repurposing of known clin -approved drugs and virtual screening for the mols available from chem libraries that may not work well For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approx 50 micromolar In an attempt to address this challenge, on Jan 28th, 2020 Insilico Medicine decided to utilize a part of its generative chem pipeline to design novel drug-like inhibitors of 2019-nCoV and started generation on Jan 30th It utilized three of its previously validated generative chem approaches: crystal-derived pocked-based generator, homol modeling-based generation, and ligand-based generation Novel druglike compounds generated using these approaches are being published at www insilico com/ncov-sprint/ and will be continuously updated Several mols will be synthesized and tested using the internal resources;however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published mols

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Zhavoronkov, Alex, Aladinskiy, Vladimir A.

Papers

Potential 2019-nCoV 3C-like protease inhibitors designed using generative deep learning approaches