Quantitative structure-activity relationship (QSAR) modelling study of some novel carboxamide series as new anti-tubercular agents

Abstract

Background: QSAR modelling was performed on thirty-five (35) newly discovered compounds of N-(2-phenoxy) ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA) to predict their biological activities against Mycobacterium tuberculosis (MTB-H37Rv strain) by using some numerical data derived from structural and chemical features (descriptors) of the compounds. Results: At first, the structure of the compounds was accurately drawn and optimized using the Spartan 14 software at DFT level of theory with B3LYP/6-31G** basis set in a vacuum. The diverse chemometric descriptors were computed from the optimized structures using the PaDEL descriptors software, and the division of the dataset into training and test sets was done based on Kennard-Stone's algorithm. Five (5) models were generated from the training set using genetic function approximation, and model 1 was chosen as the best due to its robust internal and external validation metrics (R 2 train = 0.8563, R 2 adjusted = 0.8185, PRESS = 3.5724, average R 2 m (LOO-train) = 0.6751, Q 2 cv = 0.7534, R 2 pred ¼ 0.7543, R 2 test = 0.6993) which passed the model criteria of acceptability. 6-Bromo-N-(2-(4-bromophenoxy) ethyl)-2ethylimidazo[1,2-a] pyridine-3-carboxamide (compound 13) was used as the structural template for the in silico design due to its high pMIC, and it is within the model's chemical space. Conclusion: Based on the information obtained from model 1, six (6) designed compounds with higher antitubercular activity were obtained. Furthermore, the ADME and drug-likeness prediction of the designed molecules showed good pharmacokinetic properties which indicate the application prospect of these compounds as novel MTB-H37Rv inhibitors. This research could help the medicinal chemists and pharmaceutical practitioners in future designing and development of more potent drug candidates.

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

“…The chemical structures of the 48 molecules in the dataset were accurately drawn using ChemDraw software, then exported to Spartan 14 for initial geometry minimization using molecular mechanics force fields (MMFF). The minimized structures were further optimized at the DFT level (B3LYP/631G∗∗) to determine their most stable conformation using Spartan 14 ( Abdullahi et al., 2020a ). PaDEL Descriptor tool was then used to calculate about 2000 molecular descriptors from the optimized structures.…”

“…The construction of the QSAR model was established by employing the Material Studio software version 8.0. The Genetic Function Approximation (GFA) was used for the feature selection of the pretreated descriptors in the training set for model development ( Abdullahi et al., 2020a ), and Friedman Lack-of-Fit (LOF) was selected as the functionality while the scaled LOF smoothness parameter was set at the default of 0.5. The population sample was set to 10,000 at 1000 maximum generations and the number of top equations return was set to 1 ( Umar et al., 2019 ).…”

“…For the 2D-QSAR modelling, the GFA-MLR model building protocol of Material Studio was initially used for the feature selection of the best descriptors matrix (independent variable) to predict the dependent variable (pIC 50 ) ( Abdullahi et al., 2020a ). Subsequently, molegro data modeller (MDM) was used to generate statistical validation metrics for the 2D-QSAR model based on MLR and ANN regression analysis ( Vyas and Georrge, 2015 ).…”

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

“…The structures were converted to 3D with the subsequent initial energy minimization at the molecular mechanics force fields (MMFF) level using Spartan 14 software. The minimized structures were further optimized at the density functional theory (DFT) level with B3LYP/631G** basis set in a vacuum to ( have a more realistic structural conformation when their respective equilibrium geometries were attained [22]. The pharmaceutical data exploration laboratory software (PaDEL-Descriptor) was utilized to calculate about 2000 descriptors from the optimized structures.…”

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

“…Only compounds with leverages less than or equal to the warning (h*) are considered to be within the chemical space and their activities are reliably predicted by the model. In addition to the AD, a plot of the normalized mean distance of the model descriptors against the standardized residuals of the predicted activities of the data set (UZAIRU'S plot) was employed to detect outliers present in the data set [25].…”