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Title: In silico prediction of xenobiotic metabolism in humans

Abstract

Xenobiotic metabolism in humans is catalyzed by a few enzymes with broad substrate specificities, which provide the overall broad chemical specificity for nearly all xenobiotics that humans encounter. Xenobiotic metabolism are classified into functional group biotransformations. Based on bona fide reactions and negative examples for each reaction class, support vector machine (SVM) classifiers are built. The input to SVM is a set of atomic and molecular features to define the electrostatic, steric, energetic, geometrical and topological environment of the atoms in the reaction center under the molecule. Results show that the overall sensitivity and specificity of classifiers is around 87%.

Authors:
 [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
991265
Report Number(s):
LA-UR-09-04848; LA-UR-09-4848
TRN: US201021%%143
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: 3rd Q-Bio Conference ; August 5, 2009 ; Santa Fe, NM
Country of Publication:
United States
Language:
English
Subject:
59; ATOMS; ENZYMES; FORECASTING; FUNCTIONALS; METABOLISM; SENSITIVITY; SPECIFICITY; SUBSTRATES; VECTORS; XENOBIOTICS

Citation Formats

Mu, Fangping. In silico prediction of xenobiotic metabolism in humans. United States: N. p., 2009. Web.
Mu, Fangping. In silico prediction of xenobiotic metabolism in humans. United States.
Mu, Fangping. Thu . "In silico prediction of xenobiotic metabolism in humans". United States. https://www.osti.gov/servlets/purl/991265.
@article{osti_991265,
title = {In silico prediction of xenobiotic metabolism in humans},
author = {Mu, Fangping},
abstractNote = {Xenobiotic metabolism in humans is catalyzed by a few enzymes with broad substrate specificities, which provide the overall broad chemical specificity for nearly all xenobiotics that humans encounter. Xenobiotic metabolism are classified into functional group biotransformations. Based on bona fide reactions and negative examples for each reaction class, support vector machine (SVM) classifiers are built. The input to SVM is a set of atomic and molecular features to define the electrostatic, steric, energetic, geometrical and topological environment of the atoms in the reaction center under the molecule. Results show that the overall sensitivity and specificity of classifiers is around 87%.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2009},
month = {1}
}

Conference:
Other availability
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