QSAR Modeling of 1‐(3,3‐Diphenylpropyl)‐Piperidinyl Amides as CCR5 Modulators Using Multivariate Adaptive Regression Spline and Bayesian Regularized Genetic Neural Networks

Abstract

This study deals with developing a quantitative structure-activity relationship (QSAR) model for describing and predicting the inhibition activity of 1-(3,3-diphenylpropyl)-piperidinyl derivatives as CCR5 modulators. Applying the multiple linear regressions (MLR) and its inability in predicting the inhibition behavior showed that the interaction has no linear characteristics. To assess the nonlinear characteristics of the inhibition activity artificial neural networks (ANN) was used for data modeling. In order to select the variables needed for developing ANNs, three variable selection algorithms were used: Stepwise-MLR, genetic algorithm-partial least squares (GA-PLS), and Bayesian regularized genetic neural networks (BRGNNs). R 2 and root mean square error (RMSE) values for training (t) and leave-one-out (LOO) procedures revealed that BRGNNs is a robust algorithm for the variable selection and regression method simultaneously. Due to the black box limitation of neural networks, multivariate adaptive regression spline (MARS) technique was used for modeling. A prominent advantage of MARS with respect to ANN is its ability in interpreting of the results of the model. Q 2 LOO and R t 2 (0.982 and 0.947) reveal that MARS can describe and predict inhibition activity of these modulators and is as robust as ANN. Because the MARS model can explain the activity of molecules, it is a useful model for designing novel CCR5 inhibitors.

Design and Fabrication of Open-Tubular Array Gas Sensors Based on Conducting Polypyrrole Modified With Crown Ethers for Simultaneous Determination of Alkylamines

“…In order to correlate the sensor response to amine concentrations, Bayesian regularized artificial neural network (BRANN) [29] has been used. The BRANN models are a class of multilayer perceptron networks with high prediction power and reproducibility of results [30]. More details about this class of networks can be found in literature [31,32].…”

CS‐MINER: A Tool for Association Mining in Binding‐Database

“…It is clear and obvious that the composition of the feature space in data matrix has drastic effect on the performances of the DA techniques. Therefore, it is very necessary to select the best subset of molecular descriptors using variable selection and dimension reduction techniques 25. In CS‐MINER, two separated steps have been designed for choosing the best subset of molecular descriptors.…”

The study of the structure – anticancer activity relationship of 4-thiazolidinone derivatives using multivariate adaptive regression splines

“…У якості базового алгоритму в даному до-слідженні було вибрано багатовимірні адаптивні регресійні сплайни (multivariate adaptive regression splines -MARS). Цей метод здатен описати складні нелінійні зв'язки між структурою молекул та їх активністю і довів свою ефективність у багатьох дослідженнях [19][20][21][22][23]. Алгоритм MARS створює адитивну нелінійну модель наступного вигляду: де: ŷ -прогнозоване значення активності; а 0 -зсув моделі; M є кількістю базисних функцій, а В m та a m є m-ою базисною функцією та її коефіцієнтом [24].…”