In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods

doi:10.1021/ci5000467

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	In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods
	Li, Xiao 1,2; Chen, Lei 2; Cheng, Feixiong 2; Wu, Zengrui 2; Bian, Hanping 2; Xu, Congying 2; Li, Weihua 2; Liu, Guixia 2; Shen, Xu1 ; Tang, Yun 2
刊名	JOURNAL OF CHEMICAL INFORMATION AND MODELING
	2014-04
卷号	54 期号:4 页码:1061-1069
ISSN号	1549-9596
DOI	10.1021/ci5000467
文献子类	Article
英文摘要	Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silk methods are hence developed as an alternative. In this study, a comprehensive data set containing 12 204 diverse compounds with median lethal dose (LD50) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), kappa-nearest neighbor (kNN), and naive Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-SVMOAO model was 83.0% and 89.9% for external validation sets I and II, respectively, which showed reliable predictive accuracy for each class. In addition, some representative substructures responsible for acute oral toxicity were identified using information gain and substructure frequency analysis methods, which might be very helpful for further study to avoid the toxicity.
资助项目	863 Project[2012AA020308] ; National Natural Science Foundation of China[81373329] ; Fundamental Research Funds for the Central Universities[WY1113007]
WOS关键词	SUPPORT VECTOR MACHINES ; NITROBENZENE TOXICITY ; NEAREST-NEIGHBOR ; DRUG DISCOVERY ; CLASSIFICATION ; INHIBITORS ; QSAR ; NONINHIBITORS ; DERIVATIVES ; RATS
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000335201200005
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/277128]
专题	药理学第三研究室
通讯作者	Tang, Yun
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China 2.E China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, Shanghai 200237, Peoples R China;
推荐引用方式 GB/T 7714	Li, Xiao,Chen, Lei,Cheng, Feixiong,et al. In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2014,54(4):1061-1069.
APA	Li, Xiao.,Chen, Lei.,Cheng, Feixiong.,Wu, Zengrui.,Bian, Hanping.,...&Tang, Yun.(2014).In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods.JOURNAL OF CHEMICAL INFORMATION AND MODELING,54(4),1061-1069.
MLA	Li, Xiao,et al."In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods".JOURNAL OF CHEMICAL INFORMATION AND MODELING 54.4(2014):1061-1069.