Quantitative structure–activity relationship

About: Quantitative structure–activity relationship is a research topic. Over the lifetime, 7560 publications have been published within this topic receiving 144670 citations. The topic is also known as: QSAR & Quantitative Structure-Activity Relationship.

Path Pendeccentric Connectivity Indices: Detour Matrix Based Molecular Descriptors for QSAR/QSPR Studies, Part 2: Development of Models for TTK Inhibitory Activity of Acetamide/Carboxamide Analogs

Abstract: A pivotal role of tyrosine threonine kinase (TTK) has been established in tumor initiation, survival of genomically unstable and aneuploid cancer cells. In present study, path pendeccentric connectivity indices reported in part 1 of the manuscript were successfully applied for developing models for predicting TTK inhibitory activity of acetamide/carboxamide analogs. Diverse 2D and 3D molecular descriptors (MDs) were successfully utilized for developing models using artificial neural networks (ANN) and moving average analysis (MAA). The overall accuracy of prediction achieved for ANN and MAA based models was up to 96% for the training set and up to 92% during cross validation. The statistical utility of the said models was also evaluated through Matthews correlation coefficient, non error rate, sensitivity and intercorrelation analysis. Low IC50 values obtained for active ranges of the proposed MAA based models indicate the tremendous potential of said models for furnishing lead molecules for developing potent TTK inhibiting acetamide/carboxamide analogs. KeywoRDS Acetamide/Carboxamide Analogs, Artificial Neural Networks, Dragon Software, Moving Average Analysis, Path Pendeccentric Connectivity Indices, Tyrosine Threonine Kinase

The comparative study of four QSAR models of aromatic compounds to aquatic organisms

Abstract: Quantitative structure-activity relationships (QSAR) were developed for 43 aromatic compounds toxicity to Photobacterium phosphoreum and Daphnia magna based on four methods: octanol/water partition coefficient, liner solution energy relationship, molecular connectivity index and group contribution. Through the evaluation of four QSAR methods, LSER was proved to be the best. And it applied to the widest range of chemicals with the greatest accuracy.

Quantitative structure-activity relationship to elucidate human CYP2A6 inhibition by organosulfur compounds

Abstract: CYP2A6 is a human enzyme responsible for the metabolic elimination of nicotine, and it is also involved in the activation of procarcinogenic nitrosamines, especially those present in tobacco smoke. Several investigations have reported that reducing this enzyme activity may contribute to anti-smoking therapy as well as reducing the risk of promutagens in the body. For these reasons, several authors investigate selective inhibitors molecules toward this enzyme. The aim of this study was to evaluate the interactions between a set of organosulfur compounds and the CYP2A6 enzyme by a quantitative structure-activity relationship (QSAR) analysis. The present work provides a better understanding of the mechanisms involved, with the final goal of providing information for the future design of CYP2A6 inhibitors based on dietary compounds. The reported activity data were modeled by means of multiple regression analysis (MLR) and partial least-squares (PLS) techniques. The results indicate that hydrophobic and steric factors govern the union, while electronic factors are strongly involved in the case of monosulfides.