First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery

Amin, Sk. Abdul; Banerjee, S.; Singh, Samayaditya; Qureshi, Insaf Ahmed; Gayen, Shovanlal; Jha, Tarun

doi:10.1007/s11030-020-10166-3

Cited by 43 publications

(49 citation statements)

References 51 publications

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“…A QSAR model for SARS-CoV-2 main protease inhibitor, built on 40 compounds, reported that the topological surface area, molecular weight, XLogP, hydrogen bond donors, hydrogen bond acceptors descriptors were needed to create the model that exhibited r 2 test = 0.753 (Islam et al 2020 ). Another model, constructed from 25 compounds, utilized solute hydrogen bond acidity, mordred autocorrelation, molecular distance edge, and two fingerprint descriptors: unsaturated non-aromatic heteroatom-containing ring size 6, and O=C–C–C–C–C–N, this model had r 2 = 0.944 (Amin et al 2021 ). Thus, the presented regression model introduces new variables that can predict the binding interaction of compounds with the viral enzyme.…”

Section: Resultsmentioning

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%

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A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors

Janairo

2021

Netw Model Anal Health Inform Bioinforma

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The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The optimized multiple linear regression model, which was trained and tested on 226 natural compounds demonstrates reliable prediction performance (r 2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors. The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors

Janairo

2021

Netw Model Anal Health Inform Bioinforma

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“…Furthermore, all newly designed compounds are confirmed to have good ADMET properties through the webserver prediction. Also, compared to recent efforts to identify promising attributes for previous coronavirus inhibitors, the present study deals with the existing SARS-CoV-2 M pro inhibitors and avoids activity test deviation between different data sets [21] . Taken together, the results from this study provide a reference for the further rational design of novel SARS-CoV-2 M pro inhibitors with high potency.…”

Section: Discussionmentioning

confidence: 99%

“…The synthesized compounds tested in the animal model have more significance to accelerate the development of novel therapies against COVID-19. Amin et al [21] performed the first structure-activity relationship analysis on the structure-activity data of recently reported diverse SARS-CoV-2 M pro inhibitors to understand the structural requirements for higher inhibitory activity. However, the activity deviation between different data sets is not considered and it is unavoidable.…”

Section: Introductionmentioning

confidence: 99%

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Luo

Tong

Zhang

et al. 2022

Journal of Molecular Structure

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The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 M pro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 M pro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 M pro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.

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“…SARS-CoV-2 is a member of the betacoronavirus family, together with SARS-CoV and Middle East Respiratory Syndrome (MERS-CoV). The enormous scientific effort worldwide led to a better understanding of SARS-CoV-2 structure and the infection mechanism, spotting four main druggable targets, namely the Spike (S) protein, Papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the main protease/3C-like protease (Mpro/3CLpro) [ 3 , 4 ]. In particular, SARS-CoV-2 Mpro leads a crucial role in the viral replication process.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

Mekni

Coronnello

Langer

et al. 2021

IJMS

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In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

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First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery

Cited by 43 publications

References 51 publications

A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors

A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

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