Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.

Mateus S.M Serafim,Vinicius G Maltarollo,Kathia M Honorio,Patricia R Oliveira,Jadson C Gertrudes,Débora M.A Costa

doi:10.1042/bsr20202616

Abstract

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

Highlights

In late December 2019 at Wuhan (China), an unknown acute respiratory disease was reported
Machine learning (ML) techniques are a valuable new tool for drug discovery against severe acute respiratory syndrome (SARS)-CoV-2, since they can be applied to build predictive models based on previous experience
We present the main aspects of machine learning (ML) approaches that may accelerate COVID-19 drug discovery, such as Ensemble Learning, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL) models [105,106,107]

Summary

Introduction

In late December 2019 at Wuhan (China), an unknown acute respiratory disease was reported. It is interesting to mention that in silico virtual screening approaches associated with structural and biophysical techniques can help the design of specific inhibitors to SARS-CoV-2, and significantly enhance the quality of compounds selected for in vitro and in vivo bioassays, increasing the success of drug discovery [31,32,33,34].

Results

Conclusion