Pathology

In silico

Disease

Reverse vaccinology

Coronavirus disease 2019 (COVID-19)

Infectious disease (medical specialty)

Bioinformatics

Biomedicine

Biology

Pandemic

Gene

Drug repositioning

Ecology

Data science

Drug

Computer science

Repurposing

Computational biology

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Biochemistry

Medicine

Pharmacology

Informatics in Medicine Unlocked

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis