ASTER: an integration of the AQUIRE data base and the QSAR system for use in ecological risk assessments

Russom, Christine L.; Anderson, Eric B.; Greenwood, Brad E.; Pilli, Anne

doi:10.1016/0048-9697(91)90219-5

Cited by 63 publications

(43 citation statements)

References 3 publications

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“…The set of 469 chemicals used in this study was obtained from the Assessment Tools for the Evaluation of Risk (ASTER) database 19 and represents a subset of the Toxic Substances Control Act (TSCA) Inventory 20 for which vapor pressure (p vap ) was measured at 25 °C with a pressure range of approximately 3 -10 000 mm Hg. The molecular weights of the compounds in this data set range from 40 to 338, and the chemical diversity is described in Table 1.…”

Section: Experimental Datamentioning

confidence: 99%

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Basak¹,

Mills²

2005

Arkivoc

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Vapor pressure is an important property which is an indicator of chemical volatility, along with transport, partitioning, fate and distribution of environmental pollutants. Various models have been developed for the prediction of vapor pressure of chemicals using physicochemical and calculated structural properties. We have used different classes of graph theoretic indices, e.g., topostructural indices, topochemical indices, geometrical (3D) indices and, quantum chemical descriptors, for the development of predictive models for vapor based on a structurally diverse set of 469 chemicals. Initially, a set of 379 molecular descriptors was calculated using the software POLLY, Triplet, Sybyl, MOPAC, and Molconn-Z. Comparatively, three linear regression methodologies were used to develop hierarchical QSAR (HiQSAR) models, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression. The results indicate that, in general, RR outperforms PCR and PLS, and that the easily calculated topological descriptors are sufficient for the prediction of vapor pressure based on this large, diverse set of chemicals.

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Section: Experimental Datamentioning

confidence: 99%

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Basak¹,

Mills²

2005

Arkivoc

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“…83 These calculated values were all obtained from the USEPA's ASTER (ASsessment Tools for the Evaluation of Risk) system. 45 Unfortunately both of these sets are very limited, but this serves to illustrate just how difficult it can be to find experimental data for chemicals of toxicological interest. Qualitatively, our results on the selection of analogs using physicochemical and topological spaces show that for any particular query chemical the selected set of neighbors is essentially the same with some minor variation, though the order of neighbor selection differs.…”

Section: Physicochemical Properties Vis-à-vis Topological Descriptorsmentioning

confidence: 99%

“…This subset represents the graph distinct molecules for which boiling point, vapor pressure, heat of vaporization, water solubility, adsorption coefficient, and logP were all available from the ASTER system. 45 However, even for the reduced set of 166 compounds, most of the data values available from ASTER were calculated, not experimental values.…”

Section: Jp-8 Mixture Databasementioning

confidence: 99%

“…This set, first analyzed by Basak and Grunwald, 55 represents the intersection of a subset of the TSCA inventory (~10,000 chemicals), melting point, boiling point, and logP data available from the ASTER system, 45 and a database of solvatochromic parameters consisting of α (hydrogen bond donor acidity), β (hydrogen bond acceptor basicity), V/100 (molar volume), and π (polarizability). The solvatochromic parameters were provided by Kamlet.…”

Section: Industrial Pollutantsmentioning

confidence: 99%

“…This data comes from the ASTER (Assessment Tools for the Evaluation of Risk) database 45 developed by the USEPA from data available in the Toxic Substances Control Act (TSCA) Inventory.…”

Section: Tsca Data From Astermentioning

confidence: 99%

See 2 more Smart Citations

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

Basak¹,

Gute²,

Mills³

2006

Arkivoc

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This account summarizes Dr. Subhash Basak's work in the field of molecular similarity. In particular, it looks at the development and application of quantitative molecular similarity analysis (QMSA) techniques using physicochemical properties, topological indices, and atom pairs as descriptors for developing structure-or property-based similarity spaces and the use of a k-nearest neighbors (kNN) technique to estimate the properties or activities of chemicals within the space. Additionally, the account discusses the novel tailored similarity technique pioneered by Dr. Basak's research group and discusses the future of molecular similarity analysis techniques.

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Quantitative structure-activity relationships for the developmental toxicity of haloacetic acids in mammalian whole embryo culture

Richard

Hunter

1996

Teratology

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Developmental toxicity in mouse whole embryo culture assay has been reported for acetic acid (AA) and a series of ten haloacetic acids, including mono‐, di‐, tri‐fluoro (MFA, DFA, TFA), chloro (MCA, DCA, TCA), bromo (MBA, DBA, TBA), and monoiodo (MIA) acetic acids. Benchmark concentrations (BCm), calculated as the lower 95% confidence limit of molar acid concentration producing a 5% increase in embryos with neural tube defects, provided potency estimates for development of quantitative structure‐activity relationships (QSARs). The best overall regression was obtained for the ten halo‐acids (excluding AA) and related log (1/BCm) to the energy of the lowest unoccupied molecular orbital (Elumo) and acid dissociation constant (pKa) with a correlation coefficient of r = 0.97, and a sample size‐adjusted r2 = 0.92. This QSAR suggested a common basis for the mechanism of HA activity, which would imply additivity for mixtures of these acids. Examination of QSARs for subsets of the total data set (e.g., monohaloacids) highlighted parameter relationships embedded in the total QSAR, helping to unravel the separate contributions of Elumo and pKa to the overall potency. The relevance of these parameters is discussed in terms of postulated mechanisms of developmental toxicity involving changes in intercellular pH and redox metabolism. The whole embryo assay results pertain to direct embryo exposure and toxicity without the confounding influence of maternal factors. The resulting QSAR model offers possible insight into the mechanism of embryo toxicity that will hopefully contribute to understanding of the more complex, in vivo teratogenicity problem. © 1996 Wiley‐Liss, Inc.

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ASTER: an integration of the AQUIRE data base and the QSAR system for use in ecological risk assessments

Cited by 63 publications

References 3 publications

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

Quantitative structure-activity relationships for the developmental toxicity of haloacetic acids in mammalian whole embryo culture

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