In silico screening of estrogen-like chemicals based on different nonlinear classification models.
Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens.
Liu
Huanxiang
H
Department of Structural and Functional Biology, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, via Dunant 3, 21100 Varese, Italy.
Papa
Ester
E
Walker
John D
JD
Gramatica
Paola
P
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D004353:Drug Evaluation, Preclinical / Q000379:methods* / Q000706:statistics & numerical data; D052244:Endocrine Disruptors / Q000737:chemistry* / Q000145:classification / Q000494:pharmacology*; D045166:Estradiol Congeners / Q000737:chemistry* / Q000145:classification / Q000494:pharmacology*; D005260:Female; D006801:Humans; D066298:In Vitro Techniques; D051188:Knowledge Bases; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D051381:Rats; D011960:Receptors, Estrogen / Q000187:drug effects / Q000378:metabolism; D012984:Software
D052244:Endocrine Disruptors; D045166:Estradiol Congeners; D011960:Receptors, Estrogen