The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity

Andrea Colombo, Emilio Benfenati, Mati Karelson, Uko Maran

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (Rcv 2 ≈ 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

Original languageEnglish
Pages (from-to)772-780
Number of pages9
JournalChemosphere
Volume72
Issue number5
DOIs
Publication statusPublished - Jun 2008

Fingerprint

Quantitative Structure-Activity Relationship
Toxicity
toxicity
Chemical Models
Pharmacologic Actions
Toxic Actions
Cyprinidae
Chemical compounds
chemical compound
Carbon
prediction
Set theory
theoretical study
Atoms
chemical
structure-activity relationship
carbon

Keywords

  • Fathead minnow
  • Maximum bond order
  • Multi-linear regression
  • QSAR
  • Toxicity prediction

ASJC Scopus subject areas

  • Environmental Chemistry
  • Environmental Science(all)

Cite this

The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity. / Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko.

In: Chemosphere, Vol. 72, No. 5, 06.2008, p. 772-780.

Research output: Contribution to journalArticle

Colombo, Andrea ; Benfenati, Emilio ; Karelson, Mati ; Maran, Uko. / The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity. In: Chemosphere. 2008 ; Vol. 72, No. 5. pp. 772-780.
@article{62825a4baa234d4ba7e90343b1aed471,
title = "The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity",
abstract = "One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (Rcv 2 ≈ 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.",
keywords = "Fathead minnow, Maximum bond order, Multi-linear regression, QSAR, Toxicity prediction",
author = "Andrea Colombo and Emilio Benfenati and Mati Karelson and Uko Maran",
year = "2008",
month = "6",
doi = "10.1016/j.chemosphere.2008.03.016",
language = "English",
volume = "72",
pages = "772--780",
journal = "Chemosphere",
issn = "0045-6535",
publisher = "Elsevier Limited",
number = "5",

}

TY - JOUR

T1 - The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity

AU - Colombo, Andrea

AU - Benfenati, Emilio

AU - Karelson, Mati

AU - Maran, Uko

PY - 2008/6

Y1 - 2008/6

N2 - One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (Rcv 2 ≈ 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

AB - One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (Rcv 2 ≈ 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

KW - Fathead minnow

KW - Maximum bond order

KW - Multi-linear regression

KW - QSAR

KW - Toxicity prediction

UR - http://www.scopus.com/inward/record.url?scp=44149098225&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44149098225&partnerID=8YFLogxK

U2 - 10.1016/j.chemosphere.2008.03.016

DO - 10.1016/j.chemosphere.2008.03.016

M3 - Article

C2 - 18471854

AN - SCOPUS:44149098225

VL - 72

SP - 772

EP - 780

JO - Chemosphere

JF - Chemosphere

SN - 0045-6535

IS - 5

ER -