QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods

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 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, R² _test and Q²cv (R² ​= ​0.838, R² _test ​= ​0.735, Q² _cv ​= ​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 (E_HOMO), energy of molecular orbital below HOMO energy (E_HOMO-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.

Keywords: ADMET; Coronavirus; Density functional theory (DFT); Disulfide; Quantitative structure activity relationship (QSAR); SARS-CoV.

Fig. 1

Williams plot of standardized residual versus leverage for the best MLR model (model 34) ( train samples in black color and test samples in red color). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Correlations of observed and predicted activities values calculated using model 34 (training set in blue and test set in red). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)