In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

Abstract

A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

Evaluating Terrestrol A as an Inhibitor Against SARS‐CoV‐2and Invasive Fungal Pathogens: A Comprehensive Computational Analysis

“…Subsequently, 30 independent instantaneous runs were executed for each ligand to enhance the docking accuracy. Further analysis was conducted to assess the docking scores and interactions between the ligands and the receptors [70–74] …”

“…Further analysis was conducted to assess the docking scores and interactions between the ligands and the receptors. [70][71][72][73][74]…”

Pharmacokinetic predictions and docking studies of substituted aryl amine-based triazolopyrimidine designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH)

“…The 2-dimensional structures of the sixteen (16) designed derivatives of substituted aryl amine-based triazolopyrimidine [18] presented in Table 1 were sketched with ChemDraw Ultra 12.0. These structures were then opened in Spartan'14 version 1.1.2 software in a 3dimensional format and are optimized on density functional theory, DFT/B3LYP, and a basis set of 6-31+G*.…”

Computer-aided molecular design of 2-anilino 4-amino substituted quinazolines derivatives as malarial inhibitors

“…Building the model applicability domain involves plotting the leverages of each compound against their respective standardized residuals. The diagonals of the hat matrix, H i = X i X T i X i −1 X T i produces the leverages for each of the compounds [12], with H i , the training/test hat matrix, X i as the initial matrix of test/training set, and X T i as the training/ test set transpose matrix. The domain has warning leverage, h * = 3(t + 1)∕z , where z and t stand for the training set and model descriptors counts respectively.…”

“…where j stands for the coefficient of j, D j stands for the training set value of the matrix descriptors and m, the descriptors sum in the model, and n is the training set count [12].…”