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CORAL: Monte Carlo Method as a Tool for the Prediction of the Bioconcentration Factor of Industrial Pollutants
Author(s) -
Toropova A. P.,
Toropov A. A.,
Martyanov S. E.,
Benfenati E.,
Gini G.,
Leszczynska D.,
Leszczynski J.
Publication year - 2013
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200069
Subject(s) - bioconcentration , quantitative structure–activity relationship , coral , pollutant , calibration , monte carlo method , computer science , graph , data mining , test set , statistics , environmental chemistry , chemistry , mathematics , machine learning , theoretical computer science , bioaccumulation , ecology , biology , organic chemistry
The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR modeling of the bioconcentration factor in fish (logBCF). The data used include 237 organic substances (industrial pollutants). Six random splits of the data into sub-training (30-50 %), calibration (20-30 %), test (13-30 %), and validation sets (7-25 %) have been carried out. The following numbers display the average statistical characteristics of the models for the external validation set: correlation coefficient r(2) =0.880±0.017 and standard error of estimation s=0.559±0.131. The best models were obtained with a combined representation of the molecular structure by SMILES together with hydrogen suppressed graph.