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Dr. Ibrahim Zaghloul Abdelbaky :: Publications:

Title:
Recent omics-based computational methods for COVID-19 drug discovery and repurposing
Authors: Hilal Tayara, Ibrahim Abdelbaky and Kil To Chong
Year: 2021
Keywords: SARS-CoV-2, COVID-19, drug discovery, omics data analysis, deep learning
Journal: Briefings in Bioinformatics
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Oxford University Press
Local/International: International
Paper Link:
Full paper Ibrahim Zaghloul Abdelbaky_bbab339.pdf
Supplementary materials Not Available
Abstract:

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

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