Chemometrics and Intelligent Laboratory Systems
Available online 3 February 2021, 104266
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QSAR study of unsymmetrical aromatic Disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods
Author links open overlay panelSamirChtitaaAssiaBelhassanbMohamedBakhouchcAbdelali IdrissiTaouratidAdnaneAouidatebSalahBelaidieMohammedMoutaabbidaSaidBelaaouadaMohammedBouachrinebfTaharLakhlifib
a
Laboratory of physical chemistry of materials, Faculty of sciences Ben M’Sik, Hassan II University of Casablanca, B.P. 7955, Sidi Othmane, Casablanca, Morocco
b
Molecular Chemistry and Natural Substances Laboratory, Department of chemistry, Faculty of sciences, University Moulay Ismail, Meknes, Morocco
c
Laboratory of Bioorganic Chemistry, Department of Chemistry, Faculty of Sciences, Chouaïb Doukkali University, 24000, El Jadida, Morocco
d
Laboratory of biological chemistry applied to the environment, Department of chemistry, Faculty of sciences, University Moulay Ismail, Meknes, Morocco
e
Laboratory of Molecular Chemistry and Environment, Group of Computational and pharmaceutical Chemistry, University of Biskra, BP145 07000, Biskra, Algeria
f
High School of Technology of Khenifra, Sultane Slimane University, B.P. 591, Khenifra, Morocco
Received 5 October 2020, Revised 9 January 2021, Accepted 30 January 2021, Available online 3 February 2021.
https://doi.org/10.1016/j.chemolab.2021.104266
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Highlights
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Quantum descriptors were calculated using DFT calculation for forty disulfide derivatives as inhibitors of the SARS Coronavirus.
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Hundred linear pentavariate and quadrivariate robust validated models were established
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Models were validated internally as well as externally along with Y-Randomization according to the OECD principles and acceptance criteria of Golbraikh and Tropsha’s
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Anti-SARS-CoV main protease of studied compounds is governed by five descriptors
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Structural requirements for great SARS-CoV inhibitor will be by substituting disulfides with smaller size electron withdrawing groups.
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New compounds with good SARS-CoV inhibitors activities have been designed.
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In silico prediction studies on ADMET pharmacokinetics properties were conducted.
Abstract:
In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311+G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV-2 activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha’s. Model 34 is chosen with higher values of R2, R2test and Q2cv (R2 = 0.838, R2test= 0.735, Q2cv = 0.757).
It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted