Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow
文献类型：期刊论文
| 作者 | Wang, Y.1; Zheng, M.1; Xiao, J.1; Lu, Y.1; Wang, F.1; Lu, J.1; Luo, X.1; Zhu, W.1; Jianga, H.2; Chen, K.1 |
| 刊名 | SAR AND QSAR IN ENVIRONMENTAL RESEARCH
 |
| 出版日期 | 2010 |
| 卷号 | 21期号:5-6页码:559-570 |
| 关键词 | toxicity prediction fathead minnow quantitative structure-activity relationship support vector regression genetic algorithm |
| ISSN号 | 1062-936X |
| DOI | 10.1080/1062936X.2010.502300 |
| 文献子类 | Article |
| 英文摘要 | The potential toxicity of chemicals may present adverse effects to the environment and human health. The quantitative structure-activity relationship (QSAR) provides a useful method for hazard assessment. In this study, we constructed a QSAR model based on a highly heterogeneous data set of 571 compounds from the US Environmental Protection Agency, for predicting acute toxicity to the fathead minnow (Pimephales promelas). An approach coupling support vector regression (SVR) with the genetic algorithm (GA) was developed to build the model. The generated QSAR model showed excellent data fitting and prediction abilities: the squared correlation coefficients (r2) for the training set and the test set were 0.826 and 0.802, respectively. Only eight critical descriptors, most of which are closely related to the toxicity mechanism, were chosen by GA-SVR, making the derived model readily interpretable. In summary, the successful case reported here highlights that our GA-SVR approach can be used as a general machine learning method for toxicity prediction. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; AQUATIC TOXICITY ; PIMEPHALES-PROMELAS ; QSAR MODELS ; ORGANIC-CHEMICALS ; NONPOLAR NARCOSIS ; DESIGN ; FISH ; DESCRIPTORS ; VALIDATION |
| 资助项目 | Hi-TECH Research and Development Program of China[2006AA10A201] ; Hi-TECH Research and Development Program of China[2006AA020402] ; National ST Major Project[2009ZX09301-001] ; State Key Program of Basic Research of China[2009CB918502] ; MOST[2007DFB30370] |
| WOS研究方向 | Chemistry ; Computer Science ; Environmental Sciences & Ecology ; Mathematical & Computational Biology ; Toxicology |
| 语种 | 英语 |
| WOS记录号 | WOS:000281592100011 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/279058]  |
| 专题 | 药物发现与设计中心 中科院受体结构与功能重点实验室 新药研究国家重点实验室 药物化学研究室 药理学第一研究室 |
| 通讯作者 | Luo, X. |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 200031, Peoples R China; 2.E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Y.,Zheng, M.,Xiao, J.,et al. Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow[J]. SAR AND QSAR IN ENVIRONMENTAL RESEARCH,2010,21(5-6):559-570. |
| APA | Wang, Y..,Zheng, M..,Xiao, J..,Lu, Y..,Wang, F..,...&Chen, K..(2010).Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow.SAR AND QSAR IN ENVIRONMENTAL RESEARCH,21(5-6),559-570. |
| MLA | Wang, Y.,et al."Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow".SAR AND QSAR IN ENVIRONMENTAL RESEARCH 21.5-6(2010):559-570. |
入库方式： OAI收割
来源：上海药物研究所
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