Estimation of acute oral toxicity in rat using local lazy learning
文献类型：期刊论文
| 作者 | Lu, Jing1,2; Peng, Jianlong2; Wang, Jinan2; Shen, Qiancheng2; Bi, Yi1; Gong, Likun2 ; Zheng, Mingyue2; Luo, Xiaomin2; Zhu, Weiliang2; Jiang, Hualiang2,3,4
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| 刊名 | JOURNAL OF CHEMINFORMATICS
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| 出版日期 | 2014-05-16 |
| 卷号 | 6 |
| 关键词 | Acute toxicity Local lazy learning Applicability domain Consensus model |
| ISSN号 | 1758-2946 |
| DOI | 10.1186/1758-2946-6-26 |
| 文献子类 | Article |
| 英文摘要 | Background: Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. Results: In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R-2 of 0.712 on a test set containing 2,896 compounds. Conclusion: Encouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via www.dddc.ac.cn/admetus. |
| WOS关键词 | MOLECULAR SIMILARITY ANALYSES ; PLASMA-PROTEIN BINDING ; APPLICABILITY DOMAIN ; QSAR MODELS ; PREDICTION |
| 资助项目 | Hi-TECH Research and Development Program of China[2012AA020308] ; National ST Major Project[2012ZX09301-001-002] ; National Natural Science Foundation of China[81220108025] ; National Natural Science Foundation of China[81001399] ; National Natural Science Foundation of China[2013ZX09507001] |
| WOS研究方向 | Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000336991100001 |
| 出版者 | BMC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/277072]  |
| 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 药物安全性评价中心 |
| 通讯作者 | Zheng, Mingyue |
| 作者单位 | 1.Yantai Univ, Sch Pharm, Dept Med Chem, Yantai 264005, Shandong, Peoples R China; 2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China; 3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China; 4.E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Jing,Peng, Jianlong,Wang, Jinan,et al. Estimation of acute oral toxicity in rat using local lazy learning[J]. JOURNAL OF CHEMINFORMATICS,2014,6. |
| APA | Lu, Jing.,Peng, Jianlong.,Wang, Jinan.,Shen, Qiancheng.,Bi, Yi.,...&Chen, Kaixian.(2014).Estimation of acute oral toxicity in rat using local lazy learning.JOURNAL OF CHEMINFORMATICS,6. |
| MLA | Lu, Jing,et al."Estimation of acute oral toxicity in rat using local lazy learning".JOURNAL OF CHEMINFORMATICS 6(2014). |
入库方式： OAI收割
来源：上海药物研究所
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