title: Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors.
abstract: Rapidly and correctly identifying endocrine-disrupting chemicals (EDCs) is an important issue in environmental risk assessment. Major EDCs are associated with the androgen receptor (AR) and oestrogen receptors (ERs). Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable alternative tools for the identification of EDCs. In this study, a large dataset related to EDCs was constructed. Each molecule was represented with seven fingerprints, and computational models were subsequently developed to predict AR and ER binders via machine learning methods including k-nearest neighbour (kNN), C4.5 decision tree (C4.5 DT), naïve Bayes (NB), and support vector machine (SVM) algorithms. The best model for predicting AR binders was PubChem Fingerprint-SVM, which exhibited an accuracy of 0.84. For ER binders, the best method was Extended Fingerprint-SVM with an accuracy of 0.79. Moreover, several representative substructure alerts for characterizing EDCs, such as phenol, trifluoromethyl, and annelated rings, were identified using the combination of information gain and substructure frequency analysis. Our study involved a systematic computational assessment of EDCs related to AR and ERs, and provides significant information on the structural characteristics of these chemicals, which are a great help in identifying EDCs.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Lu
authors.initials: L
authors.lastname: Sun
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. Electronic address: ytang234@ecust.edu.cn.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D052244:Endocrine Disruptors / Q000032:analysis / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008956:Models, Chemical; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D011944:Receptors, Androgen / Q000737:chemistry / Q000378:metabolism*; D011960:Receptors, Estrogen / Q000737:chemistry / Q000378:metabolism*; D012307:Risk Factors; D054928:Validation Studies as Topic
chemical_list: D052244:Endocrine Disruptors; D011944:Receptors, Androgen; D011960:Receptors, Estrogen
keywords: Androgen receptor; Endocrine-disrupting chemicals; Machine learning; Oestrogen receptors; Substructure alert