In silico Prediction of Chemical Ames Mutagenicity
文献类型：期刊论文
| 作者 | Xu, Congying2; Cheng, Feixiong2; Chen, Lei2; Du, Zheng2; Li, Weihua2; Liu, Guixia1,2; Lee, Philip W.2; Tang, Yun2 |
| 刊名 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
 |
| 出版日期 | 2012-11 |
| 卷号 | 52期号:11页码:2840-2847 |
| ISSN号 | 1549-9596 |
| DOI | 10.1021/ci300400a |
| 文献子类 | Article |
| 英文摘要 | Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness. in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis. of this data set, high predictive models were then built using five machine learning methods, namely support Vector machine (SVM), C4.5 decision free (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naive Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys.(MACCS) PubChem fingerprint (PubChem) and Substructure fingerprint (SubFP). Performances were measured by cross Validation and an external test set containing 831 diverse chemicals. Information.:Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold Cross Validation were from 0.808 to 0.841 for top five Models. The, range of accuracy:for the,external validation set was from 0.904 to 0.980, which outperformed that of Toxtree., Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable,predictive accuracy. for the mutagens and nonmutagens and, hence, could he used in prediction of chemical Ames Mutagenicity: |
| WOS关键词 | CLASSIFICATION MODELS ; NAIVE BAYES ; QSAR ; FINGERPRINTS ; INHIBITORS ; NONINHIBITORS ; CARCINOGENS ; DISCOVERY ; ALERTS ; ASSAY |
| 资助项目 | 863 Project[2012AA020308] ; Fundamental Research Funds for the Central Universities[WY1113007] ; State Key Laboratory of Drug Research[SIMM1203KF-13] ; Shanghai Committee of Science and Technology[11DZ2260600] |
| WOS研究方向 | Pharmacology & Pharmacy ; Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000311461400005 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/277883]  |
| 专题 | 新药研究国家重点实验室 中科院受体结构与功能重点实验室 |
| 通讯作者 | Liu, Guixia |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Drug Res, 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 | Xu, Congying,Cheng, Feixiong,Chen, Lei,et al. In silico Prediction of Chemical Ames Mutagenicity[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2012,52(11):2840-2847. |
| APA | Xu, Congying.,Cheng, Feixiong.,Chen, Lei.,Du, Zheng.,Li, Weihua.,...&Tang, Yun.(2012).In silico Prediction of Chemical Ames Mutagenicity.JOURNAL OF CHEMICAL INFORMATION AND MODELING,52(11),2840-2847. |
| MLA | Xu, Congying,et al."In silico Prediction of Chemical Ames Mutagenicity".JOURNAL OF CHEMICAL INFORMATION AND MODELING 52.11(2012):2840-2847. |
入库方式： OAI收割
来源：上海药物研究所
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