Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs

被引:1087
|
作者
Eriksson, L
Jaworska, J
Worth, AP
Cronin, MTD
McDowell, RM
Gramatica, P
机构
[1] Umetr AB, S-90719 Umea, Sweden
[2] Procter & Gamble Eurocor, Cent Prod Safety, Strombeek Bever, Belgium
[3] European Commiss, Joint Res Ctr, Inst Hlth & Consumer Protect, European Chem Bur, Ispra, Italy
[4] Liverpool John Moores Univ, Sch Pharm & Chem, Liverpool L3 5UX, Merseyside, England
[5] USDA, Anim & Plant Hlth Inspect Serv, Risk Anal Syst, Riverdale, MD USA
[6] Insubria Univ, Dept Struct & Funct Biol, QSAR & Environm Chem Res Unit, Varese, Italy
关键词
QSAR acceptability criteria; QSAR applicability domain; QSAR reliability; QSAR uncertainty estimation; QSAR validation;
D O I
10.1289/ehp.5758
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.
引用
收藏
页码:1361 / 1375
页数:15
相关论文
共 50 条
  • [1] Development of classification- and regression-based QSAR models and in silico screening of skin sensitisation potential of diverse organic chemicals
    Nandy, Ashis
    Kar, Supratik
    Roy, Kunal
    MOLECULAR SIMULATION, 2014, 40 (04) : 261 - 274
  • [2] Regression-based classification methods and their comparison with decision tree algorithms
    Kiselev, MV
    Ananyan, SM
    Arseniev, SB
    PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY, 1997, 1263 : 134 - 144
  • [3] Analysis and evaluation of regression-based methods for facial pose classification
    Jaiswal, Ajay
    Kumar, Nitin
    Agrawal, R. K.
    INTERNATIONAL JOURNAL OF APPLIED PATTERN RECOGNITION, 2015, 2 (01) : 24 - 45
  • [4] Watershed size effects on applicability of regression-based methods for fluvial loads estimation
    Kumar, Saurav
    Godrej, Adil N.
    Grizzard, Thomas J.
    WATER RESOURCES RESEARCH, 2013, 49 (11) : 7698 - 7710
  • [5] A REGRESSION-BASED LINEAR CLASSIFICATION PROCEDURE
    LAMOTTE, LR
    MCWHORTER, A
    EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 1981, 41 (02) : 341 - 347
  • [6] Critical assessment of regression-based machine learning methods for polymer dielectrics
    Mannodi-Kanakkithodi, Arun
    Pilania, Ghanshyam
    Ramprasad, Rampi
    COMPUTATIONAL MATERIALS SCIENCE, 2016, 125 : 123 - 135
  • [7] REGRESSION-BASED FORECAST COMBINATION METHODS
    Wei, Xiaoqiao
    ROMANIAN JOURNAL OF ECONOMIC FORECASTING, 2009, 12 (04): : 5 - 18
  • [8] Diagnostic tools to determine the quality of "transparent" regression-based QSARs: The "modelling power" plot
    Sagrado, S
    Cronin, MTD
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2006, 46 (03) : 1523 - 1532
  • [9] Matrix Regression-Based Classification for Face Recognition
    Mi, Jian-Xun
    Zhu, Quanwei
    Luo, Zhiheng
    STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, S+SSPR 2018, 2018, 11004 : 357 - 366
  • [10] Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models
    Toplak, Marko
    Mocnik, Rok
    Polajnar, Matija
    Bosnic, Zoran
    Carlsson, Lars
    Hasselgren, Catrin
    Demsar, Janez
    Boyer, Scott
    Zupan, Blaz
    Stalring, Jonna
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2014, 54 (02) : 431 - 441