QSAR can modify the molecular structures for achieving the desired molecule with the proposed property, without experimental measurement. In the current study, we extend a published work that had been investiged the caffeic acid derivatives as antibacterial and antifungal agents. In this report, QSAR and regression analysis were used to predicate the antimicrobial activity of these derivatives....

.................................................................................................. 3 INTRODUCTION......................................................................................... 3 QSAR TECHNIQUES .................................................................................. 3 CALCULATION OF MOLECULAR DESCRIPTORS............................ 3 STATISTICAL GENERATION O...

QSAR studies were performed to understand the structure activity relationship (SAR) and to build the computational model to predict newer inhibitors with improved potency. In this study, a library of thiophene-anthranilamide based inhibitors of factor Xa was used to develop QSAR model. The library was divided into two sets: Training and Test sets. QSAR Model consists of four descriptors with R-...

On the basis of the previous models of inductive and steric effects, ‘inductive’ electronegativity and molecular capacitance, a range of new ‘inductive’ QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intraand intermolecular i...

Quantitative structure-activity relationships (QSAR) is an area of computational research that builds virtual models to predict quantities such as the binding affinity or the toxic potential of existing or hypothetical molecules. Although a wealth of experimental data emphasizes the active role of the target protein in the binding process, QSAR studies are frequently restricted to the propertie...

Most quantitative structure-activity relationship (QSAR) models are linear relationships and significant for only a limited domain of compounds. Here we propose a data-driven approach with a flexible combination of unsupervised and supervised neural networks able to predict the toxicity of a large set of different chemicals while still respecting the QSAR postulates. Since QSAR is applicable on...

A set of 26 compound of the series halogenated Indole-3-Acetic acids as oxidatively activate prodrugs with potential for targeted cancer therapy were subjected to quantitative structure activity relationship (QSAR) analysis using combination of various electronic, thermodynamic and spatial descriptors. Several statistical regression equations were obtained using multiple regression analysis. QS...

The prediction of properties of molecules from their structure (QSAR) is basically a nonlinear regression problem. Neural networks are proven to be parsimonious universal approximators of nonlinear functions; therefore, they are excellent candidates for performing the nonlinear regression tasks involved in QSAR. However, their full potential can be exploited only in the framework of a rigorous ...

Based on descriptors of n-octanol/water partition coefficients (logKow), molecular connectivity indices, and quantum chemical parameters, several QSAR models were built to estimate the soil sorption coefficients (logKoc) of substituted anilines and phenols. Results showed that descriptor logKow plus molecular quantum chemical parameters gave poor regression models. Further study was performed t...