In-Silico Studies of Some Indole Derivatives as an Anti-Hepatitis C Drug.

Abstract

A combined three-dimensional quantitative structure-activity relationship (QSAR) modeling and molecular docking studies were carried out on the 64 indole derivatives and was accomplished to profoundly understand the structure-activity correlation of indole-based inhibitors of the HCV NS5B polymerase against HCV. Genetic function approximation (GFA) of Material studio software version 8 was used to perform the QSAR study while Autodock vina version 4.0 of Pyrx software was used for molecular docking studies of the selected indole derivatives. The optimum model builds exhibited statistically significant results: squared correlation coefficient (R 2) of 0.760, adjusted squared correlation coefficient (R 2 adj) value of 0.708, Leave one out (LOO) cross-validation coefficient value of 0.634 and the external validation (R 2 pred) of 0.621. Molecular docking study of the indole derivative with 1G8Q as the protein target revealed that the best binding affinity with the docking scores of-9.4 kcal/mol formed hydrophobic interaction and H-bonding with amino acid residues of HCV NS5B polymerase. The QSAR model generated and molecular docking results proposed that the model had a good level of stability, strength, and predictability at internal and external validation, and the physicochemical parameters are to be analyzed when designing new indole derivatives agent with better activity against the 1G8Q target site. k e y w o r d s QSAR Molecular Docking Indole NS5B polymerase HCV Binding Energy j c e c JCEC

Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches

“…Computational tools have gained tremendous significance in drug discoveries and design processes (Baldi, 2010;Ismail et al, 2019;Elshakre et al, 2020). Computational chemistry approach is often employed routinely for the study of drug-receptor complexes in atomic details and in calculating the properties of small-molecular drug candidates (Adedirin et al, 2018;Bello et al, 2018;Elshakre et al, 2020). Tool from information sciences and statistics are progressively important for organizing and managing the huge biological and chemical activity databases which are now possess by most pharmaceutical companies, so as to make the optimum use of these databases (Perez-Castillo et al, 2018).…”