Predicting toxicity through a computer automated structure evaluation program.

Klopman, Gilles

doi:10.1289/ehp.8561269

Cited by 29 publications

(2 citation statements)

References 20 publications

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“…For example, suppose a toxicity database exists on a series of N chemical agents, and it is of interest to study the characteristics of Q ≥ 1 putative potency estimators. Suppose also that the database already contains summary QSAR values for each chemical, taken from a known source such as Klopman's (1985) Computer-automated Structure Evaluation (CASE) technique. Viewing these summary values as pairs (x i , y qi ) = (QSAR i , potency qi ), q = 1, .…”

Section: Data Mining Potency Estimatorsmentioning

confidence: 99%

Data Mining Potency Estimators From Toxicological Databases

Piegorsch¹,

Simmons

Zeiger³

2004

Bulletin of Informatics and Cybernetics

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We discuss use of data mining techniques to study estimators of toxic potency (activity) in toxicological databases. The methods are slight variations on the standard data mining motif, but fit fully within the larger context of knowledge discovery in databases. An example illustrates the general theme of the approach, using results from a U.S. National Toxicology Program Salmonella mutagenicity database.

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Section: Data Mining Potency Estimatorsmentioning

confidence: 99%

Data Mining Potency Estimators From Toxicological Databases

Piegorsch¹,

Simmons

Zeiger³

2004

Bulletin of Informatics and Cybernetics

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“…Current quantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) models use computer modeling of the molecule or break the molecule into secondary structural pieces (Cramer et al, 1988a;Tong et al, 1997;Kellogg et al, 1996;Hansch and Leo, 1995;Klopman 1984Klopman , 1992. Many calculations are used in QSAR, SOM or E-states models.…”

Section: Introductionmentioning

confidence: 99%

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroids binding to the aromatase enzyme

Beger

Wilkes

2002

J of Molecular Recognition

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A method that combines NMR spectral and structural information into a constructed three-dimensional (3D)-connectivity matrix is developed for modeling biological binding activity of small molecules. The 3D-connectivity matrix for a molecule is defined by associating the distances between all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts. In this project we selected from the total 3D-connectivity matrix a subset, the two-dimensional (2D) (13)C-(13)C COSY and a theoretical long range 2D (13)C-(13)C distance connectivity spectral plane. Patterns of (13)C chemical shifts observed at these two relative distances for 50 steroids were used to produce a mathematical relationship for the steroids' relative binding affinity (pK(i)) to the aromatase enzyme. We call this technique comparative structural connectivity spectra analysis (CoSCoSA) modeling. Using combinations of the 2D COSY and 2D long-range distance spectra as modeling parameters, we built four CoSCoSA models. One model was made from the 2D COSY spectra alone and another was developed using only the 2D long-range distance spectra. Then the COSY and long-distance spectra were combined in two different ways: starting with the combined principal components (PCs) from the separately calculated COSY and distance spectra or using the combined raw spectra (3D). The best CoSCoSA model was based on the combined PCs from COSY and distance spectra. This model had an r(2) of 0.96 and a leave-one-out cross-validation (q(2)) of 0.92. In general CoSCoSA modeling combines the quantum mechanical information inherent in NMR chemical shifts with internal molecular atom-to-atom distances to give a reliable and straightforward basis for predictive modeling. The technique has the flexibility and accuracy to outperform not only the cross-validated variance q(2) of previously published quantitative structure-activity relationships (QSAR) but also those obtained by related quantitative spectral data-activity relationships (QSDARs) lacking connectivity dimensions.

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Occupational Toxicology in the Pharmaceutical Industry

Gad

2002

Drug Safety Evaluation

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Predicting toxicity through a computer automated structure evaluation program.

Cited by 29 publications

References 20 publications

Data Mining Potency Estimators From Toxicological Databases

Data Mining Potency Estimators From Toxicological Databases

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroids binding to the aromatase enzyme

Occupational Toxicology in the Pharmaceutical Industry

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