title: In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods.
abstract: Carcinogenicity is one of the most concerned properties of chemicals to human health, thus it is important to identify chemical carcinogenicity as early as possible. In this study, 829 diverse compounds with rat carcinogenicity were collected from Carcinogenic Potency Database (CPDB). Using six types of fingerprints to represent the molecules, 30 binary and ternary classification models were generated to predict chemical carcinogenicity by five machine learning methods. The models were evaluated by an external validation set containing 87 chemicals from ISSCAN database. The best binary model was developed by MACCS keys and kNN algorithm with predictive accuracy at 83.91 %, while the best ternary model was also generated by MACCS keys and kNN algorithm with overall accuracy at 80.46 %. Furthermore, the best binary and ternary classification models were used to estimate carcinogenicity of tobacco smoke components containing 2251 compounds. 981 ones were predicted as carcinogens by binary classification model, while 110 compounds were predicted as strong carcinogens and 807 ones as weak carcinogens by ternary classification model. The results indicated that our models would be helpful for prediction of chemical carcinogenicity.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.|Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, P. R. China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Du
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.
authors.forename: Zengrui
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.
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, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, P. R. China.
authors.forename: Xu
authors.initials: X
authors.lastname: Shen
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033. ytang234@ecust.edu.cn.|Key Laboratory of Cigarette Smoke, Technical Center, Shanghai Tobacco Group Co. Ltd. Shanghai 200082, P. R. China. ytang234@ecust.edu.cn.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D002273:Carcinogens / Q000737:chemistry* / Q000145:classification*; D003198:Computer Simulation; D062126:Databases, Chemical; D006801:Humans; D008956:Models, Chemical; D014028:Tobacco Smoke Pollution
chemical_list: D002273:Carcinogens; D014028:Tobacco Smoke Pollution
keywords: Binary classification; Chemical carcinogenicity; Machine learning methods; Ternary classification; Tobacco smoke