Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods

Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods <jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to predict the octanol/water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) of 43 organophosphorous compounds.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.</jats:p> </jats:sec> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management of Environmental Quality: An International Journal CrossRef

Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods

Management of Environmental Quality: An International Journal , Volume 28 (4): 579-592 – Jun 12, 2017

Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods


Abstract

<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The purpose of this paper is to predict the octanol/water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) of 43 organophosphorous compounds.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.</jats:p>
</jats:sec>

Loading next page...
 
/lp/crossref/modeling-and-prediction-of-octanol-water-partition-coefficient-of-iJjs2ErsqM
Publisher
CrossRef
ISSN
1477-7835
DOI
10.1108/meq-08-2015-0162
Publisher site
See Article on Publisher Site

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to predict the octanol/water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) of 43 organophosphorous compounds.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (<jats:italic>K</jats:italic><jats:sub>ow</jats:sub>) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.</jats:p> </jats:sec>

Journal

Management of Environmental Quality: An International JournalCrossRef

Published: Jun 12, 2017

References

Sorry, we don’t have permission to show this article on DeepDyve,
but here are related articles that you can start reading right now:

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$499/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month