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D B Turner

Publications and source records attributed to D B Turner.

10 recordsLinked to original sources
PubMed2001

Automatic generation of alignments for 3D QSAR analyses.

Many 3D QSAR methods require the alignment of the molecules in a dataset, which can require a fair amount of manual effort in deciding upon a rational basis for the superposition. This paper describes the use of FBSS, a program for field-based similarity searching in chemical databases, for generating such alignments automatically. The CoMFA and CoMSIA experiments with several literature datasets show that the QSAR models resulting from the FBSS alignments are broadly comparable in predictive performance with the models resulting from manual alignments.

Computer Simulation↗
PubMed2000

The EVA spectral descriptor.

The EVA descriptor is derived from fundamental IR and Raman range molecular vibrational frequencies. EVA is sensitive to 3-D structure, but has an advantage over field-based 3-D QSAR methods inasmuch as it is invariant to both translation and rotation of the structures concerned and thus structural superposition is not required. The latter property and the demonstration of the effectiveness of the descriptor for QSAR means that EVA has been the subject of a great deal of interest from the modelling community. This review describes the derivation of the descriptor, details its main parameters and how to apply them, and provides an overview of the validation that has been done with the descriptor. A recent enhancement to the technique is described which involves the localised adjustment of variance in such a way that enhanced internal and external predictability may be obtained. Despite the statistical quality of EVA QSAR models, the main draw-back to the descriptor at present is the difficulty associated with back-tracking from a PLS model to an EVA pharmacophore. Brief comment is made on the use of the EVA descriptor for diversity studies and the similarity searching of chemical structure databases.

Spectrum Analysis↗
PubMed2000

Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA).

The EVA structural descriptor, based upon calculated fundamental molecular vibrational frequencies, has proved to be an effective descriptor for both QSAR and database similarity calculations. The descriptor is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as structural superposition is not required. The original technique involves a standardisation method wherein uniform Gaussians of fixed standard deviation (sigma) are used to smear out frequencies projected onto a linear scale. The smearing function permits the overlap of proximal frequencies and thence the extraction of a fixed dimensional descriptor regardless of the number and precise values of the frequencies. It is proposed here that there exist optimal localised values of sigma in different spectral regions; that is, the overlap of frequencies using uniform Gaussians may, at certain points in the spectrum, either be insufficient to pick up relationships where they exist or mix up information to such an extent that significant correlations are obscured by noise. A genetic algorithm is used to search for optimal localised sigma values using crossvalidated PLS regression scores as the fitness score to be optimised. The resultant models were then validated against a previously unseen test set of compounds and through data scrambling. The performance of EVA_GA is compared to that of EVA and analogous CoMFA studies; in the latter case a brief evaluation is made of the effect of grid resolution upon the stability of CoMFA PLS scores particularly in relation to test set predictions.

Algorithms↗
PubMed1999

Evaluation of a novel molecular vibration-based descriptor (EVA) for QSAR studies: 2. Model validation using a benchmark steroid dataset.

The EVA molecular descriptor derived from calculated molecular vibrational frequencies is validated for use in QSAR studies. EVA provides a conformationally sensitive but, unlike 3D-QSAR methods such as CoMFA, superposition-free descriptor that has been shown to perform well with a wide range of datasets and biological endpoints. A detailed study is made using a benchmark steroid dataset with a training/test set division of structures. Intensive statistical validation tests are undertaken including various forms of crossvalidation and repeated random permutation testing. Latent variable score plots show that the distribution of structures in reduced dimensional space can be rationalized in terms of activity classes and that EVA is sensitive to structural inconsistencies. Together, the findings indicate that EVA is a statistically robust means of detecting structure-activity correlations with performance entirely comparable to that of analogous CoMFAs. The EVA descriptor is shown to be conformationally sensitive and as such can be considered to be a 3D descriptor but with the advantage over CoMFA that structural superposition is not required. EVA has the property that in certain situations the conformational sensitivity can be altered through the appropriate choice of the EVA sigma parameter.

Models, Molecular↗
PubMed1997

Evaluation of a novel infrared range vibration-based descriptor (EVA) for QSAR studies. 1. General application.

A novel molecular descriptor (EVA) based upon calculated infrared range vibrational frequencies is evaluated for use in QSAR studies. The descriptor is invariant to both translation and rotation of the structures concerned. The method was applied to 11 QSAR datasets exhibiting both a range of biological endpoints and various degrees of structural diversity. This study demonstrates that robust QSAR models can be obtained using the EVA descriptor and examines the effect of EVA parameter changes on these models; recommendations are made as to the appropriate choice of parameters. The performance of EVA was found to be comparable in statistical terms to that of CoMFA, despite the fact that EVA does not require the generation of a structural alignment. Models derived using semiempirical (MOPAC AM1 and PM3) and AMBER mechanics calculated normal mode frequencies are compared, with the overall conclusion that the semiempirical methods perform equally well and both outperform the AMBER-based models.

Computer Simulation↗
PubMed1997

Identification of an accurate soil suspension/dispersion modeling method for use in estimating health-based soil cleanup levels of hexavalent chromium in chromite ore processing residues.

The primary health concern associated with chromite ore processing residues (COPR) at sites in Hudson County, NJ, is the inhalation of Cr(VI) suspended from surface soils. Since health-based soil standards for Cr(VI) will be derived using the inhalation pathway, soil suspension modeling will be necessary to estimate site-specific, health-based soil cleanup levels (HBSCLs). The purpose of this study was to identify the most appropriate particulate emission and air dispersion models for estimating soil suspension at these sites based on their theoretical underpinnings, scientific acceptability, and past performance. The identified modeling approach, the AP-42 particulate emission model and the fugitive dust model (FDM), was used to calculate concentrations of airborne Cr(VI) and TSP at two COPR sites. These estimated concentrations were then compared to concentrations measured at each site. The TSP concentrations calculated using the AP-42/FDM soil suspension modeling approach were all within a factor of 3 of the measured concentrations. The majority of the estimated air concentrations were greater than the measured, indicating that the AP-42/FDM approach tends to overestimate on-site concentrations. The site-specific Cr(VI) HBSCLs for these two sites calculated using this conservative soil suspension modeling approach ranged from 190 to 420 mg/kg.

Chromium↗
PubMed1980

Transport and fate of gaseous pollutants associated with the National Energy Program.

The experimental evidence related to the long-range transport and transformations of sulfates has been critically reviewed. It has been concluded that sulfate emissions from various sources can be superimposed on each other during long-range transport to create episodes far from the contributing sources. Sulfates are important contributors to degrading visibility through the United States including relatively pristine areas in the western United States. Acid sulfate components, along with nitric acid, may increase the problem of acidity in precipitation on soils, vegetation, and lakes in certain areas of the eastern United States. Improvements in monitoring of these pollutants are required to follow changes in air quality caused by long-range transport.

Air Pollutants↗