Recent omics-based computational methods for COVID-19 drug discovery and repurposing

Tayara, Hilal; Abdelbaky, Ibrahim; Chong, Kil To

doi:10.1093/bib/bbab339

Cited by 11 publications

(8 citation statements)

References 148 publications

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“…(3) In addition to structural data of proteins and drugs, the availability of omics or clinical data can support drug discovery or repurposing. AI models can find hidden patterns and relations and offer more accurate prediction by using big data with different scales and types [ 330 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Computational Biology and Artificial Intelligence in Drug Design

Zhang

Luo

et al. 2022

IJMS

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Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

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Section: Discussionmentioning

confidence: 99%

Application of Computational Biology and Artificial Intelligence in Drug Design

Zhang

Luo

et al. 2022

IJMS

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“…Due to its dependence on the host response and its indirect measurement of SARS-CoV-2 infection and replication, the CPE assay may have certain limitations. The phenotypic outcome can also vary depending on culture conditions, the viral multiplicity of infection (MOI), and the number of virions added per cell during infection [43][44][45].…”

Section: Phenotypic Screeningmentioning

confidence: 99%

A Comprehensive Review of Drug Repurposing Strategies against Known Drug Targets of COVID-19

Khataniar

Pathak

Rajkhowa

et al. 2022

COVID

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Drug repurposing is a more inexpensive and shorter approach than the traditional drug discovery and development process. The concept of identifying a potent molecule from a library of pre-existing molecules or an already approved drug has become a go-to tactic to accelerate the identification of drugs that can prevent COVID-19. This seemingly uncontrollable disease is caused by SARS-CoV-2. It is a novel virus of the Betacoronavirus genus, exhibiting similarities to the previously reported SAR-CoV genome structure and viral pathogenesis. The emergence of SARS-CoV-2 and the rapid outbreak of COVID-19 have resulted in a global pandemic. Researchers are hard-pressed to develop new drugs for total containment of the disease, thus making the cost-effective drug repurposing a much more feasible approach. Therefore, the current review attempts to collate both the experimental and computational drug repurposing strategies that have been utilized against significant drug targets of SARS-CoV-2. Along with the strategies, the available druggable targets shall also be discussed. However, the occurrence of frequent recombination of the viral genome and time-bound primary analysis, resulting in insignificant data, are two major challenges that drug repurposing still faces.

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“…Recently, thanks to a tremendous increase in the availability of bioactive macromolecules, advances in understanding of interactions among small molecules, and advances in technologies for computer-aided antibody design, the applicability of computational analysis has been broadly extended to cover nearly every stage in the process of drug discovery and development. This includes identification and validation of the target of interest, lead discovery, assay optimization, preclinical evaluation, and clinical development (1)(2)(3)(4)(5). Computational drug discovery methods have evolved significantly over the past decades.…”

Section: Introductionmentioning

confidence: 99%