Quantitative structure-activity relationships (QSARs) provide a description of the correlation between the structure of a molecule and a specific molecular property of interest. QSARs can be employed to refine the search for molecules matching a desired property in an existing database, but ideally one would like to examine potential compounds outside the database through solving the inverse-QS...

Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one invo...

Adequate conformational searching of small molecules and inclusion of a chirality identifier are necessary features of any current technique for quantitative structure-activity relationships (QSAR). However, implementation of these features can be difficult and computationally expensive, and some techniques can still lead to insufficient treatment of molecular conformation. We select the standa...

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...