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大连理工大学 [5]
上海药物研究所 [3]
四川大学 [2]
中南大学 [2]
北京大学 [1]
兰州大学 [1]
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期刊论文 [18]
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A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling
期刊论文
JOURNAL OF CHEMINFORMATICS, 2021, 卷号: 13, 期号: 1, 页码: 17
作者:
Wang, Dingyan
;
Yu, Jie
;
Chen, Lifan
;
Li, Xutong
;
Jiang, Hualiang
收藏
  |  
浏览/下载:61/0
  |  
提交时间:2021/11/04
Uncertainty quantification
Quantitative structure-activity relationship
Bayesian neural network
Applicability domain
Bayesian inference
Error prediction
Artificial intelligence
Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine
期刊论文
Biochimica et biophysica acta-general subjects, 2016, 卷号: 1860, 期号: 11, 页码: 2664-2671
作者:
Lu, Jing
;
Lu, Dong
;
Zhang, Xiaochen
;
Bi, Yi
;
Cheng, Keguang
收藏
  |  
浏览/下载:49/0
  |  
提交时间:2019/05/09
Elimination half-life
Gradient boosting machine
Applicability domain
Consensus model
Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine
期刊论文
BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS, 2016, 卷号: 1860, 期号: 11, 页码: 2664-2671
作者:
Lu, Jing
;
Lu, Dong
;
Zhang, Xiaochen
;
Bi, Yi
;
Cheng, Keguang
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2019/01/08
Elimination half-life
Gradient boosting machine
Applicability domain
Consensus model
Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine
期刊论文
BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS, 2016, 卷号: 1860, 期号: 11, 页码: 2664-2671
作者:
Lu, Jing
;
Lu, Dong
;
Zhang, Xiaochen
;
Bi, Yi
;
Cheng, Keguang
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/16
Elimination half-life
Gradient boosting machine
Applicability domain
Consensus model
Model population analysis in chemometrics
期刊论文
Chemometrics and Intelligent Laboratory Systems, 2015, 卷号: 149, 页码: 166-176
作者:
Deng, Bai-Chuan
;
Yun, Yong-Huan
;
Liang, Yi-Zeng*
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/03
Model population analysis
Chemometrics
Variable selection
Model evaluation
Outlier detection
Applicability domain
Estimation of acute oral toxicity in rat using local lazy learning
期刊论文
JOURNAL OF CHEMINFORMATICS, 2014, 卷号: 6
作者:
Lu, Jing
;
Peng, Jianlong
;
Wang, Jinan
;
Shen, Qiancheng
;
Bi, Yi
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2019/01/08
Acute toxicity
Local lazy learning
Applicability domain
Consensus model
A Combinational Strategy of Model Disturbance and Outlier Comparison to Define Applicability Domain in Quantitative Structural Activity Relationship
期刊论文
Molecular Informatics, 2014, 卷号: 33, 期号: 8, 页码: 503-513
作者:
Yan, Jun
;
Zhu, Wei-Wei
;
Kong, Bo
;
Lu, Hong-Bing
;
Yun, Yong-Huan
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/03
Quantitative structure‐activity relationship
Applicability domain
Model disturbance index
Outlier comparison
Retention index
Global Performance and Trend of QSAR/QSPR Research: A Bibliometric Analysis
期刊论文
2014
Li, Li
;
Hu, Jianxin
;
Ho, Yuh-Shan
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2015/11/10
Quantitative structure-activity relationship (QSAR)
Quantitative structure-property relationship (QSPR)
Research trend
SCI-EXPANDED
Scientometrics
SCIENCE-CITATION-INDEX
MOLECULAR SIMILARITY INDEXES
HIV-1 INTEGRASE INHIBITORS
COMSIA 3D QSAR
BIOLOGICAL-ACTIVITY
ELECTROTOPOLOGICAL-STATE
THEORETICAL DESCRIPTORS
PARTITION-COEFFICIENTS
APPLICABILITY DOMAIN
RISK-ASSESSMENT
Development and validation of theoretical linear solvation energy relationship models for toxicity prediction to fathead minnow (pimephales promelas)
期刊论文
CHEMOSPHERE, 2014, 卷号: 96, 页码: 188-194
作者:
Lyakurwa, Felichesmi
;
Yang, Xianhai
;
Li, Xuehua
;
Qiao, Xianliang
;
Chen, Jingwen
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/12/09
TLSER models
Fathead minnow
Electron donor-acceptor interaction
Mode of action
Applicability domain
Development of in silico models for predicting LSER molecular parameters and for acute toxicity prediction to fathead minnow (Pimephales promelas)
期刊论文
CHEMOSPHERE, 2014, 卷号: 108, 页码: 17-25
作者:
Lyakurwa, Felichesmi Selestini
;
Yang, Xianhai
;
Li, Xuehua
;
Qiao, Xianliang
;
Chen, Jingwen
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/09
Mode of action
LSER parameters
QSAR
Acute toxicity
Applicability domain
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