title: Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.
abstract: Elimination half-life is an important pharmacokinetic 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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).
  
  
  
  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.
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, 32 Qingquan Road, Yantai 264005, PR China. Electronic address: lujing_ytu@126.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Lu
authors.affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, PR China; Stake Key Laboratory of Natural and Biomimetic Drugs, Peking University, 38 Xueyuan Road, Beijing 100191, PR China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, PR China. Electronic address: sz_ludong@foxmail.com.
authors.forename: Dong
authors.initials: D
authors.lastname: Lu
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, 32 Qingquan Road, Yantai 264005, PR China. Electronic address: ytu_zhangxc@163.com.
authors.forename: Xiaochen
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, 32 Qingquan Road, Yantai 264005, PR China. Electronic address: beeyee_413@sina.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Bi
authors.affiliation: Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Guangxi Normal University), Ministry of Education of China, Guilin 541004, PR China. Electronic address: kgcheng2008@gmail.com.
authors.forename: Keguang
authors.initials: K
authors.lastname: Cheng
authors.affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, PR China. Electronic address: myzheng@simm.ac.cn.
authors.forename: Mingyue
authors.initials: M
authors.lastname: Zheng
authors.affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, PR China; Stake Key Laboratory of Natural and Biomimetic Drugs, Peking University, 38 Xueyuan Road, Beijing 100191, PR China. Electronic address: xmluo@simm.ac.cn.
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Luo
mesh_terms: D015195:Drug Design; D006207:Half-Life; D006801:Humans; D008954:Models, Biological; D009930:Organic Chemicals / Q000378:metabolism*; D004364:Pharmaceutical Preparations / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship
chemical_list: D009930:Organic Chemicals; D004364:Pharmaceutical Preparations
keywords: Applicability domain; Consensus model; Elimination half-life; Gradient boosting machine