2021
DOI: 10.1007/s00894-021-04703-6
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Structure-activity relationship (SAR) and molecular dynamics study of withaferin-A fragment derivatives as potential therapeutic lead against main protease (Mpro) of SARS-CoV-2

Abstract: The spread of novel coronavirus SARS-CoV-2 has directed to a state of an unprecedented global pandemic. Many synthetic compounds and FDA-approved drugs have been significantly inhibitory against the virus, but no SARS-CoV-2 solution has been identified. However, small molecule fragment–based derivatives of potent phytocompounds may serve as promising inhibitors against SARS-CoV-2. In the pursuit of exploring novel SARS-CoV-2 inhibitors, we generated small molecule fragment derivatives from potent phytocompound… Show more

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Cited by 26 publications
(13 citation statements)
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“…In silico screening of several phytochemicals identified that Withanone, one of the constituents of ashwagandha, showed a potential inhibition of ACE2 ( Balkrishna et al, 2021 ). Additionally, Ghosh et al (2021) used molecular dynamic simulations and pharmacophore modeling approaches to predict the highly potent small-molecule derivative of withaferin A that potentially inhibits SARS-CoV-2 protease (Mpro), a favorable future therapeutic against COVID-19.…”
Section: Discussionmentioning
confidence: 99%
“…In silico screening of several phytochemicals identified that Withanone, one of the constituents of ashwagandha, showed a potential inhibition of ACE2 ( Balkrishna et al, 2021 ). Additionally, Ghosh et al (2021) used molecular dynamic simulations and pharmacophore modeling approaches to predict the highly potent small-molecule derivative of withaferin A that potentially inhibits SARS-CoV-2 protease (Mpro), a favorable future therapeutic against COVID-19.…”
Section: Discussionmentioning
confidence: 99%
“…The coefficients A 1 through A n are derived by fitting the parameter and biological activity changes. These B 1 , B 2 , B 3 , etc., are the descriptor variables of the QSAR equation [ 28 ]. For this objective, molecular descriptors were derived using the SwissADME tool descriptor, and the experimental values of the training sample were obtained from a previous publication [ 29 ].…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, the multiple linear regression model built in this study was formulated using at least 200 compounds. Some of the relevant MLR models have so far utilized 100 compounds or less (Amin et al 2021 ; De et al 2020 ; Ghosh et al 2021 ; Kumar and Roy 2020 ). Deep learning and other machine learning models can screen bigger number of compounds, but they lack interpretability, which highlights the simplicity, transparency and interpretability of MLR models.…”
Section: Resultsmentioning
confidence: 99%