A QSAR study for modeling of thyroid receptors ß1 selective ligands by application of adaptive neuro-fuzzy inference system and radial basis function

A quantitative structureactivity relationship study of thyroid hormone receptors beta 1 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 beta 1 thyroid receptors. A set of 83 selective ligands with known affinity of thyroid receptors beta 1 (pIC50) 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 pIC50 of thyroid receptors beta 1 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 (c) 2012 John Wiley & Sons, Ltd.