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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
by
Pande, Veena
, Maiti, Priyanka
, Kuniyal, Jagdish Chandra
, Nand, Mahesha
, Ramakrishnan, Muthannan Andavar
, Chandra, Subhash
, Joshi, Tushar
in
631/114/2163
/ 631/154/1435/2418
/ Anti-HIV Agents - pharmacology
/ Antiviral Agents - pharmacology
/ Avian infectious bronchitis
/ Bronchitis
/ Cheminformatics - methods
/ Chymotrypsin
/ Coronavirus 3C Proteases - antagonists & inhibitors
/ Coronaviruses
/ COVID-19
/ COVID-19 - virology
/ COVID-19 Drug Treatment
/ Cysteine proteinase
/ Deep Learning
/ Drug discovery
/ Drug Evaluation, Preclinical - methods
/ Drug Repositioning - methods
/ HIV-1 - drug effects
/ Humanities and Social Sciences
/ Humans
/ Infectious bronchitis virus - drug effects
/ Learning algorithms
/ Machine learning
/ Molecular Docking Simulation
/ Molecular dynamics
/ Molecular Dynamics Simulation
/ multidisciplinary
/ Nucleotide sequence
/ Pandemics
/ Prediction models
/ Protease Inhibitors - pharmacology
/ SARS-CoV-2 - drug effects
/ SARS-CoV-2 - enzymology
/ Science
/ Science (multidisciplinary)
/ Severe acute respiratory syndrome coronavirus 2
/ Structure-function relationships
2020
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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
by
Pande, Veena
, Maiti, Priyanka
, Kuniyal, Jagdish Chandra
, Nand, Mahesha
, Ramakrishnan, Muthannan Andavar
, Chandra, Subhash
, Joshi, Tushar
in
631/114/2163
/ 631/154/1435/2418
/ Anti-HIV Agents - pharmacology
/ Antiviral Agents - pharmacology
/ Avian infectious bronchitis
/ Bronchitis
/ Cheminformatics - methods
/ Chymotrypsin
/ Coronavirus 3C Proteases - antagonists & inhibitors
/ Coronaviruses
/ COVID-19
/ COVID-19 - virology
/ COVID-19 Drug Treatment
/ Cysteine proteinase
/ Deep Learning
/ Drug discovery
/ Drug Evaluation, Preclinical - methods
/ Drug Repositioning - methods
/ HIV-1 - drug effects
/ Humanities and Social Sciences
/ Humans
/ Infectious bronchitis virus - drug effects
/ Learning algorithms
/ Machine learning
/ Molecular Docking Simulation
/ Molecular dynamics
/ Molecular Dynamics Simulation
/ multidisciplinary
/ Nucleotide sequence
/ Pandemics
/ Prediction models
/ Protease Inhibitors - pharmacology
/ SARS-CoV-2 - drug effects
/ SARS-CoV-2 - enzymology
/ Science
/ Science (multidisciplinary)
/ Severe acute respiratory syndrome coronavirus 2
/ Structure-function relationships
2020
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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
by
Pande, Veena
, Maiti, Priyanka
, Kuniyal, Jagdish Chandra
, Nand, Mahesha
, Ramakrishnan, Muthannan Andavar
, Chandra, Subhash
, Joshi, Tushar
in
631/114/2163
/ 631/154/1435/2418
/ Anti-HIV Agents - pharmacology
/ Antiviral Agents - pharmacology
/ Avian infectious bronchitis
/ Bronchitis
/ Cheminformatics - methods
/ Chymotrypsin
/ Coronavirus 3C Proteases - antagonists & inhibitors
/ Coronaviruses
/ COVID-19
/ COVID-19 - virology
/ COVID-19 Drug Treatment
/ Cysteine proteinase
/ Deep Learning
/ Drug discovery
/ Drug Evaluation, Preclinical - methods
/ Drug Repositioning - methods
/ HIV-1 - drug effects
/ Humanities and Social Sciences
/ Humans
/ Infectious bronchitis virus - drug effects
/ Learning algorithms
/ Machine learning
/ Molecular Docking Simulation
/ Molecular dynamics
/ Molecular Dynamics Simulation
/ multidisciplinary
/ Nucleotide sequence
/ Pandemics
/ Prediction models
/ Protease Inhibitors - pharmacology
/ SARS-CoV-2 - drug effects
/ SARS-CoV-2 - enzymology
/ Science
/ Science (multidisciplinary)
/ Severe acute respiratory syndrome coronavirus 2
/ Structure-function relationships
2020
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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
Journal Article
Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
2020
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Overview
COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC
50
values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Anti-HIV Agents - pharmacology
/ Antiviral Agents - pharmacology
/ Coronavirus 3C Proteases - antagonists & inhibitors
/ COVID-19
/ Drug Evaluation, Preclinical - methods
/ Drug Repositioning - methods
/ Humanities and Social Sciences
/ Humans
/ Infectious bronchitis virus - drug effects
/ Molecular Docking Simulation
/ Molecular Dynamics Simulation
/ Protease Inhibitors - pharmacology
/ Science
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