Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.
The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (MW), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.
College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China. Electronic address: zhanghui123gansu@163.com.
Hui
H
Zhang
Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China.
Zhi-Xing
ZX
Cao
College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China.
Meng
M
Li
Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China.
Yu-Zhi
YZ
Li
Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China.
Cheng
C
Peng
D000818:Animals; D001499:Bayes Theorem; D015197:Carcinogenicity Tests / Q000379:methods*; D002273:Carcinogens / Q000737:chemistry / Q000145:classification* / Q000633:toxicity*; D003198:Computer Simulation; D062126:Databases, Chemical; D015233:Models, Statistical; D009369:Neoplasms / Q000139:chemically induced*; D051381:Rats
D002273:Carcinogens
Carcinogenicity; Extended connectivity fingerprints (ECFP_14); In silico prediction; Molecular descriptors; Naïve Bayes classifier