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 IC 50 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 R 2 , R 2 test and Q 2 cv (R 2 = 0.838, R 2 test = 0.735, Q 2 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.

Currently, anti‐butyrylcholinesterase (anti‐BuChE)is among the greatest therapeutic agents for the treatment of Alzheimer's disease. In this research, a series of 36 carbamate derivatives were subjected to a quantitative structure–activity relationships study using DFT and Lipinski's descriptors. Multiple linear regression (MLR) was used to explore the relationships between the structural features of these compounds and BuChE inhibitory activity. In order to generate results applicable in the experimental plan, the Organization of Economic Cooperation and Development guidelines were adopted. The quality of MLR model was evaluated by several statistical parameters including internal and external validation parameters (R2, R2adj, F, VIF, R2test, Q2CV, R2Rand, Q2CV [Rand], and cRp2). The built model displayed a high predictive power (R2test = 0.817). What is more, the internal validation parameters (Q2CV = 0.774; average R2Rand = 0.118; average Q2CV [Rand] = −0.438; cRp2 = 0.820) highlight the robustness of our built model. Besides, the obtained result revealed that anti‐BuChE activity is mainly attributed to the following molecular descriptors: octanol–water partition (log P), highest occupied molecular orbital energy (EHOMO), total energy (ET), and dipole moment (μ). Based on these findings, a series of newer synthesizable compounds with enhanced anti‐BuChE activities were designed and their ADMET and drug‐likeness properties were further predicted to filter out compounds likely to fail during drug development stages. Finally, molecular docking and molecular dynamics were performed to identify the binding types between the best designed compounds and BuChE enzyme.

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