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    QSAR modelling of integrin antagonists using enhanced bayesian regularised genetic neural networks

    , Article SAR and QSAR in Environmental Research ; Volume 22, Issue 3-4 , May , 2011 , Pages 293-314 ; 1062936X (ISSN) Jalali Heravi, M ; Mani Varnosfaderani, A ; Sharif University of Technology
    2011
    Abstract
    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... 

    Monte Carlo sampling and multivariate adaptive regression splines as tools for QSAR modelling of HIV-1 reverse transcriptase inhibitors

    , Article SAR and QSAR in Environmental Research ; Volume 23, Issue 7-8 , Jun , 2012 , Pages 665-682 ; 1062936X (ISSN) Alamdari, R. F ; Mani Varnosfaderani, A ; Asadollahi Baboli, M ; Khalafi Nezhad, A ; Sharif University of Technology
    2012
    Abstract
    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...