Sharif Digital Repository

In this paper, genetic algorithm (GA) and stepwise multiple regression variable selection methods were used as a feature-selection tools and neural network was employed for feature mapping. To provide an extended test of these hybrid methods, a data set consists of the bioconcentration factors (BCF) for 53 molecules were selected. Suitable set of molecular descriptors were calculated and the important descriptors were selected by genetic algorithm and stepwise multiple regression methods. These variables serve as inputs to generated neural networks. After optimization and training of the networks, they were used for the calculation of bioconcentration factors for the prediction set.... 

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models. © 2001... 

The CMC of a set of51 alkylpolyoxyethylene glycol ethers, R(EO)(m), and alkylphenol (ethylene oxide) ethers, RΦ(EO)(m), was related to topological, electronic and molecular structure parameters using a stepwise regression method. In development of the models linear and quadratic terms were used without the use of cross terms. Different strategies including Akaike Information Criterion (AIC) were used for choosing the best model. Specification of the best model in agreement with the experiment indicates that volume of the hydrophobic group and surface area of the molecule play a major role in the mechanism of micellization of nonionic surfactants. It was demonstrated that the CMC of these... 

A quantitative structure-activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR... 

The main aim of the present work was the development of a quantitative structure-activity relationship method using an artificial neural network (ANN) for predicting the thermal conductivity detector response factor. As a first step a multiple linear regression (MLR) model was developed and the descriptors appearing in this model were considered as inputs for the ANN. The descriptors of molecular mass, number of vibrational modes of the molecule, molecular surface area and Balaban index appeared in the MLR model. In agreement with the molecular diameter approach, molecular mass and molecular surface area play a major role in estimating the thermal conductivity detector response factor... 

Computer-assisted methods were employed to develop a statistical relationship between molecular-based structural parameters and log critical micelle concentration (CMC) of some anionic surfactants. The CMC of 31 alkyl sulfates and alkanesulfonates were used for model generation. Among different models, two equations were selected as the best, and their specifications are given. The statistics of these models together with cross-validation results indicate the capability of both models to predict the CMC of anionic surfactants. Three descriptors of Wiener number, reciprocal of the dipole moment, and reciprocal of the Randic index appear in the models. Results indicate that topological... 

The critical micelle concentration (CMC) of a set of 30 alkyltrimethylammonium [RN+(R′)3X-] and alkylpyridinium salts [RN+φX-] was related to topological, electronic, and molecular structure parameters using a stepwise regression method. Among different models obtained, two equations were selected as the best and their specifications are given. The statistics for these models together with the crossvalidation results indicate the capability of both models to predict the CMC of cationic surfactants. The results obtained for alkyltrimethylammonium salts indicate that geometric characteristics such as volume of the tail of the molecule, maximum distance between the atoms, and surface area play... 

Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describingα 4β 7 and α 4β... 

There are, to date, few general answers to fundamental questions related to the interactions of nanoparticles (NPs) with living cells. Studies reported in the literature have delivered only limited principles about the nano-bio interface and thus the biological behavior of NPs is yet far from being completely understood. Combining computational tools with experimental approaches in this regard helps to precisely probe the nano-bio interface and allows the development of predictive and descriptive relationships between the structure and the activity of nanomaterials. In the present contribution, a nano-quantitative structure-activity relationship (nano-QSAR) model has been statistically... 

The present work focuses on the development of an interpretable quantitative structure-activity relationship (QSAR) model for predicting the anti-HIV activities of 67 thiazolylthiourea derivatives. This set of molecules has been proposed as potent HIV-1 reverse transcriptase inhibitors (RT-INs). The molecules were encoded to a diverse set of molecular descriptors, spanning different physical and chemical properties. Monte Carlo (MC) sampling and multivariate adaptive regression spline (MARS) techniques were used to select the most important descriptors and to predict the activity of the molecules. The most important descriptor was found to be the aspherisity index. The analysis of variance... 

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and... 

Quantitative structure-activity relationship (QSAR) approach was carried out for the prediction of inhibitory activity of some novel quinazolinone derivatives on serotonin (5-HT7) using modified ant colony (ACO) method and adaptive neuro-fuzzy interference system (ANFIS) combined with shuffling cross-validation technique. A modified ACO algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict 5-HT7 receptor binding activities of quinazolinone derivatives. The best descriptors describing the inhibition mechanism are Qmax, Se, Hy, PJI3 and DELS which are among electronic, constitutional, geometric and... 

A linear and non-linear quantitative structure-activity relationship (QSAR) study is presented for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 derivatives of 2,3-dihydro-1,3-dioxo-1H-isoindole-5-carboxylic acid, furanyl-1,3-thiazol-2-yl and benzoxazol-5-yl acetic acids is used in this study. Among a large number of descriptors, four parameters classified as physico-chemical, topological and electronic indices are chosen using stepwise multiple regression technique. The artificial neural networks (ANNs) model shows superiority over the multiple linear regressions (MLR) by accounting 87.9% of the variances of antiviral potency of the heparanase... 

This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role... 

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazolesubstituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure-activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x-y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R2, and... 

In this work, the inhibitory activity of pyridine N-oxide derivatives against human severe acute respiratory syndrome (SARS) is predicted in terms of quantitative structure-activity relationship (QSAR) models. These models were developed with the aid of multivariate adaptive regression spline (MARS) and adaptive neuro-fuzzy inference system (ANFIS) combined with shuffling cross-validation technique. A shuffling MARS algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict SARS inhibitory activities of pyridine N-oxide derivatives. A data set of 119 drug-like compounds was coded with over hundred calculated... 

An effect-directed analysis (EDA) of fresh and artificially weathered (evaporated, photooxidized) samples of North Sea crude oil and residual heavy fuel oil is presented. Aliphatic, aromatic, and polar oil fractions were tested for the presence of aryl hydrocarbon receptor (AhR) agonist and androgen receptor (AR) antagonist, demonstrating for the first time the AR antagonist effects in the aromatic and, to a lesser extent, polar fractions. An extension of the typical EDA strategy to include an N-way partial least-squares (N-PLS) model capable of relating the comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) data set to the bioassay data...