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A QSAR study for modeling of thyroid receptors β1 selective ligands by application of adaptive neuro‐fuzzy inference system and radial basis function
Author(s) -
Azimi Gholamhassan,
AfiuniZadeh Somaieh,
Karami Alireza
Publication year - 2012
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2421
Subject(s) - adaptive neuro fuzzy inference system , quantitative structure–activity relationship , radial basis function , artificial intelligence , molecular descriptor , linear regression , robustness (evolution) , computer science , linear model , pattern recognition (psychology) , mathematics , machine learning , fuzzy logic , chemistry , artificial neural network , fuzzy control system , biochemistry , gene
A quantitative structure–activity relationship study of thyroid hormone receptors β1 is described in this paper. We used adaptive neuro‐fuzzy inference system (ANFIS) and radial basis function (RBF) methods coupling to genetic algorithm (GA) to predict binding affinity of some ligands with β1 thyroid receptors. A set of 83 selective ligands with known affinity of thyroid receptors β1 (pIC 50 ) were selected, and a large number of molecular descriptors were calculated for each molecule by Dragon . Seven most relevant descriptors were selected by GA‐stepwise partial least squares as variable selection tool. The best descriptors (SCBO and EEig08x) and (SCBO, EEig08x, and BEHe1) were applied to train the ANFIS and RBF models, respectively. Then the number and shape of related functions were optimized. The ability and robustness of the GA‐ANFIS, GA‐RBF, and GA‐multiple linear regression (MLR) models in predicting the pIC 50 of thyroid receptors β1 are illustrated by internal validation technique of leave one out and also heuristic and randomized techniques as external validation methods. The results have indicated that the proposed models of ANFIS and RBF in this work are superior to MLR method because of generation of simpler models with only two and three descriptors, respectively. Copyright © 2012 John Wiley & Sons, Ltd.