Use of Topostructural, Topochemical, and Geometric Parameters in the Prediction of Vapor Pressure:  A Hierarchical QSAR Approach

Basak, Subhash C.; Gute, Brian D.; Grunwald, Gregory D.

doi:10.1021/ci960176d

Cited by 94 publications

(83 citation statements)

References 22 publications

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“…The accuracy of the predicted log p L values for both polar and nonpolar compounds with this QSPR model is generally better than previously reported QSPR models. 4,23 NEURAL NETWORKS NN analyses are claimed to be superior to linear regression in their ability to handle nonlinear correlations. 15 Hence, NN analyses 13 were tested on several of the vapor pressure regression models.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

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QSPR Prediction of Vapor Pressure from Solely Theoretically-Derived Descriptors

Liang¹,

Gallagher²

1998

J. Chem. Inf. Comput. Sci.

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To date, most reported quantitative structure-property relationship (QSPR) methods to predict vapor pressure rely on, at least, some empirical data, such as boiling points, critical pressures, and critical temperatures. This limits their usefulness to available chemicals and incurs the time and expense of experimentation. A model to predict vapor pressure from only computationally derived molecular descriptors, allowing study of hypothetical structures, is described here. Several multilinear regressions and artificial neural network analyses were tested with a range of descriptors (e.g., topological and quantum mechanical) derived solely from computations on molecular structure data. From a set of 479 compounds, a linear regression with an r 2 of 0.960 was achieved using polarizibility and polar functional group counts as descriptors. This new computationally based model also proves to be more accurate and works over a wider range of compound classes than most previously reported models.

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Section: Comparison Of Resultsmentioning

confidence: 99%

“…The average predicted log p L values are more accurate than some previously reported models. 4,23 Unlike previously reported methods, the QSPR models described here have the advantage of not requiring any experimental parameters and, hence, can be applied to hypothetical compounds across a broad range of compound classes.…”

Section: Resultsmentioning

confidence: 99%

QSPR Prediction of Vapor Pressure from Solely Theoretically-Derived Descriptors

Liang¹,

Gallagher²

1998

J. Chem. Inf. Comput. Sci.

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“…Basak et al formulated the hierarchical quantitative structure-activity relationship (HiQSAR) approach for the estimation of properties, biomedicinal activities, and toxicities of chemicals from computed descriptors. [6][7][8][9][10][11][12][13][14][15][16][17][18] The objective of this HiQSAR/ HiQSPR research has been twofold: description and prediction. The HiQSPR formalism uses progressively more complex indices in the development of models.…”

Section: Introductionmentioning

confidence: 99%

“…Basak et al have used the HiQSPR approach previously in the development of VP prediction models. 12,15 However, the current study utilizes an expanded set of descriptors along with three statistical modeling approaches, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression, which are appropriate for data sets wherein the number descriptors is large with respect to the number of chemical compounds and when the molecular descriptors are highly intercorrelated.…”

Section: Introductionmentioning

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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“…• Mutagenicity of aromatic and heteroaromatic amines [24,[49][50][51][52] • Complement-inhibitory activity of benzamidines [53] • Vapor pressure [54,55] • Boiling point of structurally heterogeneous chemicals [56] • Acute toxicity of benzene derivatives [57] • Biological partition coefficients [58][59][60] • Dermal penetration of polycyclic aromatic compounds [61] • Toxicity of halocarbons [62] In this section, we provide a few examples of our HiQSAR studies involving heterocyclic compounds, which are important both as drugs and toxicants.…”

Section: Qsar Modelsmentioning

confidence: 99%