In Silico Prediction of Chemical Acute Oral Toxicity Using MultiClassification Methods
文献类型：期刊论文
| 作者 | Li, Xiao1,2; Chen, Lei2; Cheng, Feixiong2; Wu, Zengrui2; Bian, Hanping2; Xu, Congying2; Li, Weihua2; Liu, Guixia2; Shen, Xu1 ; Tang, Yun2
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| 刊名 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
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| 出版日期 | 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. |
| WOS关键词 | SUPPORT VECTOR MACHINES ; NITROBENZENE TOXICITY ; NEAREST-NEIGHBOR ; DRUG DISCOVERY ; CLASSIFICATION ; INHIBITORS ; QSAR ; NONINHIBITORS ; DERIVATIVES ; RATS |
| 资助项目 | 863 Project[2012AA020308] ; National Natural Science Foundation of China[81373329] ; Fundamental Research Funds for the Central Universities[WY1113007] |
| WOS研究方向 | Pharmacology & Pharmacy ; Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000335201200005 |
| 出版者 | AMER CHEMICAL SOC |
| 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. |
入库方式： OAI收割
来源：上海药物研究所
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