title: Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens.
abstract: Volume learning algorithm (VLA) artificial neural network and partial least squares (PLS) methods were compared using the leave-one-out cross-validation procedure for prediction of relative potency of xenoestrogenic compounds to the estrogen receptor. Using Wilcoxon signed rank test we showed that VLA outperformed PLS by producing models with statistically superior results for a structurally diverse set of compounds comprising eight chemical families. Thus, CoMFA/VLA models are successful in prediction of the endocrine disrupting potential of environmental pollutants and can be effectively applied for testing of prospective chemicals prior their exposure to the environment.
authors: Institute of Bioorganic Chemistry and Petrochemistry, Kyiv, Murmanska 1, 02660, Ukraine.
authors: Vasyl V
authors: VV
authors: Kovalishyn
authors: Vladyslav
authors: V
authors: Kholodovych
authors: Igor V
authors: IV
authors: Tetko
authors: William J
authors: WJ
authors: Welsh
mesh_terms: D000465:Algorithms; D052244:Endocrine Disruptors / Q000737:chemistry / Q000378:metabolism; D004785:Environmental Pollutants / Q000737:chemistry / Q000378:metabolism; D004967:Estrogens / Q000737:chemistry* / Q000378:metabolism; D004968:Estrogens, Non-Steroidal / Q000737:chemistry / Q000378:metabolism; D016018:Least-Squares Analysis; D008958:Models, Molecular; D015394:Molecular Structure; D016571:Neural Networks, Computer; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000737:chemistry / Q000378:metabolism; D015262:Xenobiotics / Q000737:chemistry / Q000378:metabolism
chemical_list: D052244:Endocrine Disruptors; D004785:Environmental Pollutants; D004967:Estrogens; D004968:Estrogens, Non-Steroidal; D011960:Receptors, Estrogen; D015262:Xenobiotics