Molecular docking simulation of thirty-five (35) molecules of N-(2-phenoxy)ethyl imidazo[1,2-a]pyridine-3-carboxamide (IPA) with Mycobacterium tuberculosis target (DNA gyrase) was carried out so as to evaluate their theoretical binding affinities. The chemical structure of the molecules was accurately drawn using ChemDraw Ultra software, then optimized at density functional theory (DFT) using Becke’s three-parameter Lee–Yang–Parr hybrid functional (B3LYP/6-311**) basis set in a vacuum of Spartan 14 software. Subsequently, the docking operation was carried out using PyRx virtual screening software. Molecule 35 (M35) with the highest binding affinity of − 7.2 kcal/mol was selected as the lead molecule for structural modification which led to the development of four (4) newly hypothetical molecules D1, D2, D3 and D4. In addition, the D4 molecule with the highest binding affinity value of − 9.4 kcal/mol formed more H-bond interactions signifying better orientation of the ligand in the binding site compared to M35 and isoniazid standard drug. In-silico ADME and drug-likeness prediction of the molecules showed good pharmacokinetic properties having high gastrointestinal absorption, orally bioavailable, and less toxic. The outcome of the present research strengthens the relevance of these compounds as promising lead candidates for the treatment of multidrug-resistant tuberculosis which could help the medicinal chemists and pharmaceutical professionals in further designing and synthesis of more potent drug candidates. Moreover, the research also encouraged the in vivo and in vitro evaluation study for the proposed designed compounds to validate the computational findings.
Background: This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC 50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target. Results: In building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R 2 train = 0.72963, R 2 adjusted = 0.67169, Q 2 cv = 0.598, R 2 pred ¼ 0.67295, R 2 test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus. Conclusion: The results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.
Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis (TB). Meanwhile, to solve the problem stated, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; AATS7s, VE2_Dzi, SpMin7-Bhe and RDF110i. The significant of the model were observed with R 2 of 0.8738, R 2 adj of 0.8351 Q_cv^2 of 0.7127 which served as criteria to substantiate the QSAR model. More also, the model significant with the QSAR external validation criterial ''(R 2 test) of 0.7532. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compounds 10 of the derivatives with promising biological activity have the utmost binding energy of -18.8 kcal/mol. Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 10). This implies that ligand 10 could be used as a structural template to design better hypothetical anti-tubercular drugs with more efficient activities. The presumption of this research aid the medicinal chemists and pharmacist to design and synthesis a novel drug candidate against the tuberculosis. Moreover, invitro and in-vivo test could be carried out to validate the computational results.
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.
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