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Title: In silico environmental chemical science: properties and processes from statistical and computational modelling

Abstract

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the abovemore » from chemicals to biologicals and materials.« less

Authors:
ORCiD logo; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1358499
Report Number(s):
PNNL-SA-126230
Journal ID: ISSN 2050-7887; 49691; KP1704020
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Environmental Science: Processes & Impacts; Journal Volume: 19; Journal Issue: 3
Country of Publication:
United States
Language:
English
Subject:
Environmental Molecular Sciences Laboratory

Citation Formats

Tratnyek, Paul G., Bylaska, Eric J., and Weber, Eric J. In silico environmental chemical science: properties and processes from statistical and computational modelling. United States: N. p., 2017. Web. doi:10.1039/c7em00053g.
Tratnyek, Paul G., Bylaska, Eric J., & Weber, Eric J. In silico environmental chemical science: properties and processes from statistical and computational modelling. United States. doi:10.1039/c7em00053g.
Tratnyek, Paul G., Bylaska, Eric J., and Weber, Eric J. Sun . "In silico environmental chemical science: properties and processes from statistical and computational modelling". United States. doi:10.1039/c7em00053g.
@article{osti_1358499,
title = {In silico environmental chemical science: properties and processes from statistical and computational modelling},
author = {Tratnyek, Paul G. and Bylaska, Eric J. and Weber, Eric J.},
abstractNote = {Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.},
doi = {10.1039/c7em00053g},
journal = {Environmental Science: Processes & Impacts},
number = 3,
volume = 19,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2017},
month = {Sun Jan 01 00:00:00 EST 2017}
}