Quantitative structure-activity relationships (QSAR) modelling on 30 N-Arylidenequinoline-3-carbohydrazides analogs was performed using Multi-Linear Regression (MLR) analysis adopting Genetic Function Algorithm (GFA) method. Semi empirical method using PM6 basis set was used for complete geometry optimization of the data set. The best model was chosen based on its statistical fit due to it good internal and external validations. From the Williams plot, it can be inferred that the reported model can make prediction of new compounds that are not within the data set. The molecular docking study showed that, the most active chemical in the data set was better than the standard β-glucuronidase inhibitor both in terms of binding scores and the amino acid residues that interacted with the drug and β-glucuronidase enzyme. The Pharmacokinetic studies indicated that none of the chemicals violated any of the condition set by the Lipinski′s Rule of five which confirm the bioavailability of these chemicals. The results these findings give room for designing novel β-glucuronidase inhibitors that are highly effective.                                                Resumen. Se llevó a cabo la técnica de QSAR en 30 analogos de N-arilidenequinolina-3-carbohidrazidas mediante el analisis de regresesión lineal múltiple (MLS) adopatando el método del algoritmo de función genética (GFA). Para la optimización completa de la geometría del conjunto de datos se utilizó un método semiémpirico del conjunto de bases PM6. El mejor modelo fue elegido basado en función de su ajuste estadístico debido a su validación interna y externa. A partir de la gráfica de Williams, se puede inferir que el modelo reportado puede predecir nuevos compuestos que no se encuentran en el conjunto de datos. Este estudio de acomplamiento molecular mostró que, el químico más activo del conjunto de datos fue mejor que el inhibidor estándar β-glucuronidasa, tanto en términos de unión y en términos de  interacción de los residuos con el fármaco y la enzima β-glucuronidasa. Los estudios farmacocinéticos que indicaron que ninguno de los fármacos incumple ninguna de las condiciones establecidas por la regla de cinco de Lipinski, en donde se confirma la biodisponibilidad de estos químicos. Los resultados de los hallazgos computacionales permiten diseñar nuevos inhibidores de la β-glucuronidasa que son altamente efectivos.

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

Hepatitis C virus (HCV) NS5B RNA enzyme in HCV viral replication and has no functional equivalent in mammalian cells. In silico study was carried out to develop a Quantitative structure activity molecular docking on some selected imidazole derivatives as anti Density functional theory with B3LYP/6 optimization Five QSAR models were generated using Genetic Function Algorithm (GFA) of the material studio software version 8, in which model one (1) was selected as the best model and reported based on the validation parameter with the squared correlation coefficient (R 2) of 0.7114. Adjusted squared correlation coefficient (R 0.6458 and cross-validation coefficient (Q was subjected to external validation and was found to be R obtained from molecular docking studies shows that the compound with the be affinity of-10.7 Kcal/mol formed hydrogen bonding of (GLN hydrophobic interaction with the amino acid residues of the polymerase(NS5B polymerase) propose the direction of designing new imidazole derivatives agent with better activity against the NS5B polymerase target site.