A quantitative structure–activity relationship study on CXL017 derivatives as effective drugs for cancer treatment

Abstract

A novel quantitative structure-activity relationship study was established on a set of ethyl-2-amino-6-(3.5-dimethoxyphenyl)-4-(2-ethoxy-2-oxoethyl)-4Hchromene-3 carboxylate (CXL017) derivatives to predict their half-maximal inhibitory concentration (IC 50 ) values. A principal component analysis pattern was first used to divide the whole data set into training and test sets. Then, stepwise (SW) and genetic algorithm (GA) feature selection approaches were selected to choose the proper molecular descriptors which were then subjected to multiple linear regression (MLR). Accordingly, linear models consisting of seven and six molecular descriptors were constructed and their stability was further checked using leave-one-out (LOO) and leave group out (LGO) crossvalidation techniques. In addition, the 15 compounds of the test set were further directed to external statistical validation to assess the forecasting capability of the generated models. A simple comparison of the results obtained by

3D-QSAR Modeling on 2-Pyrimidine Carbohydrazides as Utrophin Modulators for the Treatment of Duchenne Muscular Dystrophy by Combining CoMFA, CoMSIA, and Molecular Docking Studies

“…Quantitative structure–activity relationship (QSAR) modeling has gained considerable popularity as a suitable alternative to practical studies in predicting changes in the activity of molecules by changes in their structure. − The 3D-QSAR methods have emerged as a more sophisticated approach for modeling structure–activity relationships in drug discovery. − This method considers the complete 3D representation of molecules, taking into account their steric characteristics and the associated electrostatic effects that influence their biological activity. − Investigating changes in the biological activity of molecules by changing their structure, based on quantitative relationships using computational algorithms and statistical techniques, is the goal of 3D-QSAR studies. , Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) are two prominent analysis techniques among the various methods used in 3D-QSAR. In the CoMFA technique, by preparing contour maps and examining the steric and electrostatic fields around a compound, it is possible to obtain the highest biological activity available for the molecule in a virtual Cartesian grid .…”

“…Quantitative structure–activity relationship (QSAR) modeling has gained considerable popularity as a suitable alternative to practical studies in predicting changes in the activity of molecules by changes in their structure. 11 − 14 The 3D-QSAR methods have emerged as a more sophisticated approach for modeling structure–activity relationships in drug discovery. 15 − 17 This method considers the complete 3D representation of molecules, taking into account their steric characteristics and the associated electrostatic effects that influence their biological activity.…”