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Navigating drug-like chemical space of anticancer molecules using genetic algorithms and counterpropagation artificial neural networks
, Article Molecular Informatics ; Volume 31, Issue 1 , JAN , 2012 , Pages 63-74 ; 18681743 (ISSN) ; Mani Varnosfaderani, A ; Sharif University of Technology
2012
Abstract
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,...
Classification, Similarity Analysis and Modeling of Drug Activities Using Chemometric Techniques: Introduction of Classical Relativity in Chemical Space
, Ph.D. Dissertation Sharif University of Technology ; Jalali-Heravi, Mehdi (Supervisor)
Abstract
The present research devoted to the application, development and implementation of clustering, classification and regression techniques for modeling of the biological activity of different drug and drug-like molecules. At first, the prediction ability of Bayesian regression techniques was evaluated for describing and predicting the inhibition behavior of Integrin antagonists. As a next step, the complementary local search techniques have been used for improving the performances of Bayesian regularized genetic neural network (BRGNN) algorithm. The results indicated that the pattern search algorithm has a great potential to be used as a feature selection method in Chemoinformatics. In line...
Integrated one-against-one classifiers as tools for virtual screening of compound databases: A case study with CNS inhibitors
, Article Molecular Informatics ; Volume 32, Issue 8 , 2013 , Pages 742-753 ; 18681743 (ISSN) ; Mani-Varnosfaderani, A ; Valadkhani, A ; Sharif University of Technology
2013
Abstract
A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next...