Literature DB >> 29197820

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.

Caroline Tebes-Stevens1, Jay M Patel2, Michaela Koopmans3, John Olmstead3, Said H Hilal4, Nick Pope5, Eric J Weber4, Kurt Wolfe4.   

Abstract

Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (Kow), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of Kow and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cheminformatics; Consensus; Persistent Organic Pollutants (POPs); Physicochemical property; Predictive models; QSAR

Mesh:

Substances:

Year:  2017        PMID: 29197820      PMCID: PMC6146973          DOI: 10.1016/j.chemosphere.2017.11.137

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  32 in total

1.  Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.

Authors:  I V Tetko; V Y Tanchuk; A E Villa
Journal:  J Chem Inf Comput Sci       Date:  2001 Sep-Oct

2.  Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.

Authors:  Joseph F Contrera; Edwin J Matthews; R Daniel Benz
Journal:  Regul Toxicol Pharmacol       Date:  2003-12       Impact factor: 3.271

3.  Random forest: a classification and regression tool for compound classification and QSAR modeling.

Authors:  Vladimir Svetnik; Andy Liaw; Christopher Tong; J Christopher Culberson; Robert P Sheridan; Bradley P Feuston
Journal:  J Chem Inf Comput Sci       Date:  2003 Nov-Dec

4.  Persistent organic pollutants (POPs): state of the science.

Authors:  K C Jones; P de Voogt
Journal:  Environ Pollut       Date:  1999       Impact factor: 8.071

5.  Vapor pressures and enthalpies of sublimation of 17 polychlorinated dibenzo-p-dioxins and five polychlorinated dibenzofurans.

Authors:  Xian-Wei Li; Etsuro Shibata; Eiki Kasai; Takashi Nakamura
Journal:  Environ Toxicol Chem       Date:  2004-02       Impact factor: 3.742

Review 6.  Best Practices for QSAR Model Development, Validation, and Exploitation.

Authors:  Alexander Tropsha
Journal:  Mol Inform       Date:  2010-07-06       Impact factor: 3.353

7.  Partitioning of polychlorinated biphenyls in octanol/water, triolein/water, and membrane/water systems.

Authors:  Thomas W Jabusch; Deborah L Swackhamer
Journal:  Chemosphere       Date:  2005-09       Impact factor: 7.086

8.  Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method.

Authors:  T M Martin; D M Young
Journal:  Chem Res Toxicol       Date:  2001-10       Impact factor: 3.739

9.  InChI - the worldwide chemical structure identifier standard.

Authors:  Stephen Heller; Alan McNaught; Stephen Stein; Dmitrii Tchekhovskoi; Igor Pletnev
Journal:  J Cheminform       Date:  2013-01-24       Impact factor: 5.514

10.  The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS.

Authors:  Igor V Tetko; Daniel M Lowe; Antony J Williams
Journal:  J Cheminform       Date:  2016-01-22       Impact factor: 5.514

View more
  4 in total

1.  Linking Molecular Structure via Functional Group to Chemical Literature for Establishing a Reaction Lineage for Application to Alternatives Assessment.

Authors:  William M Barrett; Sudhakar Takkellapati; Kidus Tadele; Todd M Martin; Michael A Gonzalez
Journal:  ACS Sustain Chem Eng       Date:  2019-04-15       Impact factor: 8.198

2.  Enhancing life cycle chemical exposure assessment through ontology modeling.

Authors:  David E Meyer; Sidney C Bailin; Daniel Vallero; Peter P Egeghy; Shi V Liu; Elaine A Cohen Hubal
Journal:  Sci Total Environ       Date:  2019-12-27       Impact factor: 7.963

3.  Development of a PFAS reaction library: identifying plausible transformation pathways in environmental and biological systems.

Authors:  Eric J Weber; Caroline Tebes-Stevens; John W Washington; Rachel Gladstone
Journal:  Environ Sci Process Impacts       Date:  2022-05-25       Impact factor: 5.334

4.  OPERA models for predicting physicochemical properties and environmental fate endpoints.

Authors:  Kamel Mansouri; Chris M Grulke; Richard S Judson; Antony J Williams
Journal:  J Cheminform       Date:  2018-03-08       Impact factor: 5.514

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.