Quantitative structure-activity relationship models for bee toxicity

被引:6
|
作者
Toropov, Andrey A. [1 ]
Toropova, Alla P. [1 ]
Como, Francesca [1 ]
Benfenati, Emilio [1 ]
机构
[1] Ist Ric Farmacol Mario Negri, Dept Environm Hlth Sci, IRCCS, Milan, Italy
来源
关键词
QSAR; bee toxicity; Monte Carlo method; CORAL software; descriptor selection; OPTIMAL DESCRIPTORS; RANDOM EVENT; QSAR MODEL; ORGANIC-CHEMICALS; SMILES; BALANCE; CORAL; CYTOTOXICITY; SYSTEM;
D O I
10.1080/02772248.2016.1242006
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Quantitative structure-activity relationship models for bee toxicity have been built up using CORAL software (http://www.insilico.eu/coral). The approach is based on the Monte Carlo technique. The molecular structure for the quantitative structure-activity relationship analysis has been presented by the simplified molecular input-line entry system. The so-called balance of correlations and the traditional scheme of building up quantitative structure-activity relationship models are compared in this work. The traditional scheme is based on three basic sets of compounds: training, calibration, and validation, whereas, the balance of correlations is based on four sets: active training, invisible training, calibration, and validation. As rule, the balance of correlations gives better models in comparison with the traditional scheme. The statistical characteristics of the models are quite good. Possible mechanistic interpretations and indications for the domain of applicability of these models are suggested.
引用
收藏
页码:1117 / 1128
页数:12
相关论文
共 50 条
  • [1] Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners
    Wang, YW
    Liu, HX
    Zhao, CY
    Liu, HX
    Cai, ZW
    Jiang, GB
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2005, 39 (13) : 4961 - 4966
  • [2] Quantitative structure-activity relationship models for compounds with anticonvulsant activity
    Bellera, Carolina L.
    Talevi, Alan
    [J]. EXPERT OPINION ON DRUG DISCOVERY, 2019, 14 (07) : 653 - 665
  • [3] Quantitative Structure-activity Relationship Models of Monomer Reactivity
    禹新良
    易翔
    杨辉琼
    [J]. Chinese Journal of Structural Chemistry, 2019, 38 (11) : 1867 - 1873
  • [4] Advances in quantitative structure-activity relationship models of antimalarials
    Roy, Kunal
    Ojha, Probir Kumar
    [J]. EXPERT OPINION ON DRUG DISCOVERY, 2010, 5 (08) : 751 - 778
  • [5] Quantitative Structure-activity Relationship Models of Monomer Reactivity
    Yu Xin-Liang
    Yi Xiang
    Yang Hui-Qiong
    [J]. CHINESE JOURNAL OF STRUCTURAL CHEMISTRY, 2019, 38 (11) : 1867 - 1873
  • [6] Advances in quantitative structure-activity relationship models of antioxidants
    Roy, Kunal
    Mitra, Indrani
    [J]. EXPERT OPINION ON DRUG DISCOVERY, 2009, 4 (11) : 1157 - 1175
  • [7] On the interpretation and interpretability of quantitative structure-activity relationship models
    Guha, Rajarshi
    [J]. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2008, 22 (12) : 857 - 871
  • [8] Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols
    Wang Bin
    Yu Gang
    Zhang Zulin
    Hu Hongying
    Wang Liansheng
    [J]. CHINESE SCIENCE BULLETIN, 2006, 51 (22): : 2717 - 2723
  • [9] Quantitative structure-activity relationship methods for the prediction of the toxicity of pollutants
    Satpathy, Raghunath
    [J]. ENVIRONMENTAL CHEMISTRY LETTERS, 2019, 17 (01) : 123 - 128
  • [10] Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols
    WANG Bin
    State Key Laboratory of Pollution Control and Resource Reuse
    [J]. Science Bulletin, 2006, (22) : 2717 - 2723