QSAR and Docking Studies of HCV NS3 Serine Protease Inhibitors

Cunha, Elaine F. F. da; Matos, Karina Silvia; Ramalho, Teodorico C.

doi:10.2174/1573406411309060003

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…A few regression based studies have been reported to identify inhibitors against HCV NS3 drug target [45] , [19] . Lafridi et al 2022 used the QSAR based Multiple Linear Regression (MLR) approach to study the interaction between macrocyclic inhibitors and NS3 protease.…”

Section: Discussionmentioning

confidence: 99%

“…da Cunha et al, 2013 developed QSAR based models using 93 boceprevir analogs achieving the R 2 of 0.66 for NS3 protease. They also performed molecular docking of promising analogs for binding affinity to NS3 protease [19] . In addition, we found a classification based method using 413 NS3 protease inhibitors having Matthew's correlation coefficient (MCC) of 0.79 [46] .…”

Section: Discussionmentioning

confidence: 99%

“…Some computational studies for identifying drugs for HCV have also been carried out. Like, da Cunha et al 2013 used the QSAR approach to look for NS3 protease inhibitors [19] . Venkatesan et al 2018 developed pharmacophore features based predictive models to identify HCV NS3/4A inhibitors [20] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Kamboj

Rajput

Rastogi

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Kamboj

Rajput

Rastogi

et al. 2022

Computational and Structural Biotechnology Journal

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“…The QSAR (quantitative structure activity relationship) is considered a widely used computational technique to construct prediction models which can distinguish the impact of important molecular fingerprints regulating their bioactivities and properties [19]. A variety of targets, including antioxidant [20], antibacterial [21,22], anticancer [23,24], and antiviral [25,26] activities, have been successfully modeled using QSAR models.…”

Section: Introductionmentioning

confidence: 99%

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure–activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set’s modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

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“…[18] Quantitative structureactivity/property relationship (QSAR/QSPR) is a commonly used computational technique for building predictive models that can discern the influence of significant molecular descriptors governing their biological activities and chemical properties. [16,19] Such QSAR models has been successfully employed for modeling a wide range of targets such as, antioxidant, [20][21][22] antimicrobial, [23][24][25][26] anticancer, [27,28] and antiviral [29,30] activities. As such, owing to the beneficial functions of QSAR models, herein, a large number of HCV inhibitors were used as the data set for the development of predictive models to facilitate rational drug design.…”

Section: Introductionmentioning

confidence: 99%

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

et al. 2020

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Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure–activity relationship (CSAR) model for identifying substructures giving rise to anti‐HCV activities among a set of 578 non‐redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in‐depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.

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QSAR and Docking Studies of HCV NS3 Serine Protease Inhibitors

Cited by 10 publications

References 12 publications

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

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