Development of novel in silico model for developmental toxicity assessment by using naive Bayes classifier method

被引:29
|
作者
Zhang, Hui [1 ,2 ,3 ]
Ren, Ji-Xia [2 ,3 ,4 ]
Kang, Yan-Li [1 ]
Bo, Peng [1 ]
Liang, Jun-Yu [1 ]
Ding, Lan [1 ]
Kong, Wei-Bao [1 ]
Zhang, Ji [1 ]
机构
[1] Northwest Normal Univ, Coll Life Sci, Lanzhou 730070, Gansu, Peoples R China
[2] Sichuan Univ, West China Med Sch, West China Hosp, State Key Lab Biotherapy, Chengdu 610041, Sichuan, Peoples R China
[3] Sichuan Univ, West China Med Sch, West China Hosp, Canc Ctr, Chengdu 610041, Sichuan, Peoples R China
[4] Liaocheng Univ, Coll Life Sci, Liaocheng 252059, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Developmental toxicity; In silica prediction; Naive Bayes classifier; Molecular descriptors; Extended connectivity fingerprints (ECFP_6); SAR MODELS; PREDICTION; CHEMICALS; VALIDATION; SELECTION;
D O I
10.1016/j.reprotox.2017.04.005
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naive Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:8 / 15
页数:8
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