Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens.
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.
Kovalishyn
Vasyl V
VV
Institute of Bioorganic Chemistry and Petrochemistry, Kyiv, Murmanska 1, 02660, Ukraine.
Kholodovych
Vladyslav
V
Tetko
Igor V
IV
Welsh
William J
WJ
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
D052244:Endocrine Disruptors; D004785:Environmental Pollutants; D004967:Estrogens; D004968:Estrogens, Non-Steroidal; D011960:Receptors, Estrogen; D015262:Xenobiotics