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Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine

文献类型：期刊论文

作者Lu, Jing1; Lu, Dong2,3,4; Zhang, Xiaochen1; Bi, Yi1; Cheng, Keguang5; Zheng, Mingyue2; Luo, Xiaomin2,3
刊名BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS
出版日期2016-11
卷号1860期号:11页码:2664-2671
关键词Elimination half-life Gradient boosting machine Applicability domain Consensus model
ISSN号0304-4165
DOI10.1016/j.bbagen.2016.05.019
文献子类Article
英文摘要Background: Elimination half-life is an important pharmacolcinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. Methods: In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Results: Among seven individual models, GBM showed the best performance (R-2 = 0.820 and RMSE = 0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R-2 = 0.738 and RMSE = 0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. Conclusions: An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R-2 = 0.723 and RMSE = 0.698). General significance: Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. (C) 2016 Elsevier B.V. All rights reserved.
WOS关键词MOLECULAR SIMILARITY ANALYSES ; PLASMA-PROTEIN BINDING ; APPLICABILITY DOMAIN ; QSAR MODELS ; DRUG DISCOVERY ; VECTOR MACHINE ; PREDICTION ; TOXICITY ; CLASSIFICATION
资助项目National Natural Science Foundation of China[81430084] ; National Natural Science Foundation of China[81573351] ; Hi-Tech Research and Development Program of China[2014AA01A302] ; State Key Laboratory of Natural and Biomimetic Drugs[00000000] ; Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Guangxi Normal University), Ministry of Education of China[CMEMR2015-B09] ; Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund[00000000]
WOS研究方向Biochemistry & Molecular Biology ; Biophysics
语种英语
WOS记录号WOS:000383825000007
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.183/handle/2S10ELR8/275844]  
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
通讯作者Zheng, Mingyue; Luo, Xiaomin
作者单位1.Yantai Univ, Shandong Univ, Sch Pharm,Collaborat Innovat Ctr Adv Drug Deliver, Key Lab Mol Pharmacol & Drug Evaluat,Minist Educ, 32 Qingquan Rd, Yantai 264005, Peoples R China;
2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China;
3.Peking Univ, Stake Key Lab Nat & Biomimet Drugs, 38 Xueyuan Rd, Beijing 100191, Peoples R China;
4.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China;
5.Guangxi Normal Univ, Key Lab Chem & Mol Engn Med Resources, Minist Educ China, Guilin 541004, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Jing,Lu, Dong,Zhang, Xiaochen,et al. Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine[J]. BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS,2016,1860(11):2664-2671.
APA Lu, Jing.,Lu, Dong.,Zhang, Xiaochen.,Bi, Yi.,Cheng, Keguang.,...&Luo, Xiaomin.(2016).Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS,1860(11),2664-2671.
MLA Lu, Jing,et al."Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine".BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS 1860.11(2016):2664-2671.

入库方式： OAI收割

来源：上海药物研究所

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