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primary topic: Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2
type: AcademicArticle
type: Article
abstract: The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called ``antiviral dataset'' (N = 38,428) ``drug-like antiviral dataset'' (N = 20,963) and ``anticorona dataset'' (N = 433) for this purpose. We analyzed the 433 molecules of ``anticorona dataset'' for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the ``anticorona dataset'' in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the ``anticorona dataset'' molecules. The ``antiviral dataset'' and ``drug-like antiviral dataset'' showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the ``antiviral dataset'' against the ``anticorona dataset'' demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and repli…
date: 2021
volume: 19
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
subject: QR Microbiology

American Association for the Advancement of Science (AAAS)
type: Organization

Comput. Struct. Biotechnol. J.
type: Collection

Saurabh Loharch
type: Person
given name: Saurabh
family name: Loharch

Abhinit Kumar
type: Person
given name: Abhinit
family name: Kumar

Sunil Kumar
type: Person
given name: Sunil
family name: Kumar

Raman Parkesh
type: Person
given name: Raman
family name: Parkesh

Rajesh P Ringe
type: Person
given name: Rajesh P
family name: Ringe

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primary topic: Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

QR Microbiology
type: Concept