title: In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.
abstract: Drug-induced myelotoxicity usually leads to decrease the production of platelets, red cells, and white cells. Thus, early identification and characterization of myelotoxicity hazard in drug development is very necessary. The purpose of this investigation was to develop a prediction model of drug-induced myelotoxicity by using a Naïve Bayes classifier. For comparison, other prediction models based on support vector machine and single-hidden-layer feed-forward neural network  methods were also established. Among all the prediction models, the Naïve Bayes classification model showed the best prediction performance, which offered an average overall prediction accuracy of [Formula: see text] for the training set and [Formula: see text] for the external test set. The significant contributions of this study are that we first developed a Naïve Bayes classification model of drug-induced myelotoxicity adverse effect using a larger scale dataset, which could be employed for the prediction of drug-induced myelotoxicity. In addition, several important molecular descriptors and substructures of myelotoxic compounds have been identified, which should be taken into consideration in the design of new candidate compounds to produce safer and more effective drugs, ultimately reducing the attrition rate in later stages of drug development.
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China. zhanghui123gansu@163.com.|State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China. zhanghui123gansu@163.com.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Peng
authors.initials: P
authors.lastname: Yu
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Teng-Guo
authors.initials: TG
authors.lastname: Zhang
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Yan-Li
authors.initials: YL
authors.lastname: Kang
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhao
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Yuan-Yuan
authors.initials: YY
authors.lastname: Li
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Jia-Hui
authors.initials: JH
authors.lastname: He
authors.affiliation: College of Life Science, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.|Bioactive Products Engineering Research Center for Gansu Distinctive Plants, Northwest Normal University, Lanzhou, 730070, Gansu, People's Republic of China.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D006410:Hematopoiesis / Q000187:drug effects*; D008956:Models, Chemical; D060388:Support Vector Machine; D015262:Xenobiotics / Q000009:adverse effects* / Q000737:chemistry*
chemical_list: D015262:Xenobiotics
keywords: Extended connectivity fingerprints (ECFP_6); Feed-forward neural network; In silico prediction; Myelotoxicity; Naïve Bayes classifier; Support vector machine method