Quantitative structure–activity relationship methods for the prediction of the toxicity of pollutants

被引:0
|
作者
Raghunath Satpathy
机构
[1] MITS Engineering College,Department of Biotechnology
来源
关键词
Biodegradation; Computational tools; Database; Descriptors; Environmental toxicity; Quantitative structure–activity relationship modeling; Validation methods; Risk assessment; Toxic compounds;
D O I
暂无
中图分类号
学科分类号
摘要
Continuous flow of toxic and persistent compounds to the environment is a global health issue. However, assessing the toxic effects of compounds is a difficult task, because some compounds may possess a combined effect during exposure. Moreover, toxicity evaluation by animal testing is long and costly. Alternatively, modeling of quantitative structure–activity relationships (QSAR) can be used to predict the acute toxicity of molecules. Properties of toxic compounds are computed and correlated using softwares and databases. Recently, this method has found potential applications for the risk assessment of several untested, toxic chemicals. This review focuses on quantitative structure–activity relationship modeling methods for the analysis of toxic compounds. Computational tool and databases are presented.
引用
收藏
页码:123 / 128
页数:5
相关论文
共 50 条
  • [1] Quantitative structure-activity relationship methods for the prediction of the toxicity of pollutants
    Satpathy, Raghunath
    ENVIRONMENTAL CHEMISTRY LETTERS, 2019, 17 (01) : 123 - 128
  • [2] Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols
    WANG Bin
    State Key Laboratory of Pollution Control and Resource Reuse
    ChineseScienceBulletin, 2006, (22) : 2717 - 2723
  • [3] Quantitative Structure–Activity Relationship for Prediction of the Toxicity of Phenols on Photobacterium phosphoreum
    Xiaolin Li
    Zunyao Wang
    Hongling Liu
    Hongxia Yu
    Bulletin of Environmental Contamination and Toxicology, 2012, 89 : 27 - 31
  • [4] Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols
    Wang Bin
    Yu Gang
    Zhang Zulin
    Hu Hongying
    Wang Liansheng
    CHINESE SCIENCE BULLETIN, 2006, 51 (22): : 2717 - 2723
  • [5] Quantitative Structure-Activity Relationship for Prediction of the Toxicity of Phenols on Photobacterium phosphoreum
    Li, Xiaolin
    Wang, Zunyao
    Liu, Hongling
    Yu, Hongxia
    BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2012, 89 (01) : 27 - 31
  • [7] Prediction of toxicity of phenols and anilines to algae by quantitative structure-activity relationship
    Lu, Guang-Hua
    Wang, Chao
    Guo, Xiao-Ling
    BIOMEDICAL AND ENVIRONMENTAL SCIENCES, 2008, 21 (03) : 193 - 196
  • [8] Prediction of Terpenoid Toxicity Based on a Quantitative Structure-Activity Relationship Model
    Perestrelo, Rosa
    Silva, Catarina
    Fernandes, Miguel X.
    Camara, Jose S.
    FOODS, 2019, 8 (12)
  • [9] A novel quantitative structure–activity relationship model for prediction of biomagnification factor of some organochlorine pollutants
    Mohammad Hossein Fatemi
    Elham Baher
    Molecular Diversity, 2009, 13 : 343 - 352
  • [10] Development and Evaluation of Conformal Prediction Methods for Quantitative Structure-Activity Relationship
    Xu, Yuting
    Liaw, Andy
    Sheridan, Robert P.
    Svetnik, Vladimir
    ACS OMEGA, 2024, : 29478 - 29490