Sharif Digital Repository

A Quantitative Structure-Property Relationship (QSPR) relating atom-based calculated descriptors to 13C NMR chemical shifts was developed to accurately simulate 13C NMR spectra of polyhydroxy and methoxy substituted dibenzo pyrons (xanthones). A dataset consisting of 35 xanthones was employed for the present analysis. A set of 132 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated for each of 497 unique carbon atoms in the dataset. Principal Component Analysis (PCA)-ranking and Artificial Neural Networks (ANNs) were used as descriptor selection and model building methods, respectively. Analyses of the results revealed a... 

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors. The proposed method was used for the classification of the molecules according to their therapeutic targets and activities. The selected molecular descriptors in this work define discrete areas in chemical space,... 

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... 

Quantitative Structure - Activity Relationship (QSAR) models for the inhibition action of some 1-phenylbenzimidazoles on platelet-derived growth are constructed using Genetic Algorithm and Artificial Neural Network (GA-ANN) method. The statistical parameters of R2 and root-mean-square error are 0.82 and 0.21, respectively using this method. These parameters show a considerable improvement compared to the stepwise multiple linear regression combined with ANN (stepwise MLR-ANN). Ten-fold shuffling crossvalidations are carried out to select the most important descriptors. Five descriptors of index of Balaban (J), average molecular weight (AMW), 3D-Wiener index (W3D), mean atomic van der Waals... 

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... 

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the... 

The gas chromatography retention indices of 168 pesticides were used to construct a robust quantitative structure - retention relationship (QSRR) model. After outlier detection by Cook's influence measurement, the remaining compounds were subjected to two different modeling strategies. The first one was stepwise multiple linear regression (stepwise-MLR). Results of this method revealed that 81.7 percent of variances of the response could be explained by the model. The other strategy was kernel orthogonal projection to latent structure (KOPLS). R2 and RMSE values for the prediction set established by Monte Carlo cross validation of the KOPLS were 0.906 and 0.093, respectively. Y-randomization... 

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 new Quantitative Structure-Retention Relationship (QSRR) approach was carried out for prediction of gas-liquid retention times of 124 psychiatric drugs in whole blood on fused-silica capillary column coated with crosslinked methylsilicone with nitrogen-phosphorus detection. After screening the descriptors, a total of 699 topological, geometric, and electronic descriptors (zero-to three-dimensional) representing various structural characteristics were calculated for each molecule in the dataset. Combined method of Classification and Regression Tree (CART) as a feature selection method for the extraction of four relevant descriptors and Adaptive Neuro-Fuzzy Inference System (ANFIS) as a... 

A Quantitative Structure-Retention Relationship (QSRR) study of 32 benzodiazepines is performed in this work. Two feature selection methods of Adaptive Neuro Fuzzy Inference System (ANFIS) and a stepwise regression approach adopted for the Multiple Linear Regressions (MLR) were used to predict the Liquid Chromatography-Mass Spectrometry (LC-MS) Retention Time (RT) of these compounds on a Xterra MS C-18 stationary phase. ANFIS and MLR methods were used as variable selection tools and a neural network was employed for predicting the RTs. Tbree descriptors of 3D-MoRSE-signal 06/weighted by atomic polarizabilities (Mor06p), Radial Distribution Function-1.0/weighted by atomic van der Waals... 

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... 

Human motion planning studies are of considerable importance in producing human-like trajectories for various industrial or clinical applications (e.g. assistive robots). In this case, the capability of Central Nervous System (CNS) in generating a large repertoire of actions can be inspirational to develop more efficient motion planning approaches. Here, inspired by structural and functional modularity in the CNS, a novel modular and hierarchical model is developed to plan the sit-to-stand (STS) transfer under varying environmental conditions. In this model, the planning process is distributed among several functionally simple modules. The cooperation of modules enables the model to plan the... 

Phytochemical investigation of the aerial parts of Salvia eremophila led to the isolation of a lupane triterpenoid, 3β, 20-dihydroxylupane-28-oic acid (1), together with eight other compounds, comprising three diterpene, two triterpene, two flavonoids and a steroidal glucoside. Their structures were elucidated by interpretation of their one-dimensional and two-dimensional NMR spectra and completed by the analysis of the HRESIMS data. Compounds 1, 2-4 and 8 were evaluated for their cytotoxicities against five human tumour cell lines. Compounds 1 and 3 hold a good potential for use in future studies due to their anti-cancer properties  

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... 

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... 

Molecules acting as antioxidants capable of scavenging reactive oxygen species (ROS) are of upmost importance in the living cell. Thymol derivatives exhibit various antioxidant activities and potential health benefits. Exploration of structure-radical scavenging activity (SAR) was approached with a wide range of thymol derivatives. To accomplish this task, the DPPH experimental assay along with quantum-chemical calculations were also employed for these compounds. By comparing the structural properties of the derivatives of interest, their antioxidant activity was explained by the formation of an intramolecular hydrogen bond and the presence of unsaturated double bond (–CHdouble bond; length... 

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 gas chromatography retention indices of 100 different components of essential oils, on three columns with stationary phases of different polarities, were used to develop robust quantitative structure-retention relationship (QSRR) models. Two linear models with only one variable, i.e. solvation entropy, were developed, which explain 95 and 94% of variances of the test set for dimethyl silicone and dimethyl silicone with 5% phenyl group columns, respectively. These models are extremely simple and easy to interpret, but they show higher errors compared with more robust models such as partial least square (PLS) and ridge regressions. For the third column (polyethylene glycol (PEG)), 24... 

Background: It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete. Results: In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments?... 

A quantitative structure-mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities (μ0) of a diverse dataset consisting of 115 carboxylic and sulfonic acids were investigated. A set of 1195 zero- to three-dimensional descriptors representing various structural characteristics was calculated for each molecule in the dataset. Classification and regression trees were successfully used as a descriptor selection method. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system. The root mean square errors for the calibration and prediction sets are 1.61 and 2.27, respectively,...