Identification of structural features and associated mechanisms of action for carcinogens in rats

Cunningham, Albert R.; Klopman, Gilles; Rosenkranz, Herbert S.

doi:10.1016/s0027-5107(98)00123-7

Cited by 52 publications

(21 citation statements)

References 23 publications

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“…However, the prediction of the mutagenic and carcinogenic activity of chemicals by any QSAR model in use is still little efficient. For any program, the concordance does not exceed 60-70% [12]. This is much less than the corresponding values for pharmacological activity.…”

Section: Introductionmentioning

confidence: 70%

“…These programs used both physicochemical properties and structural fragments as descriptors. The most efficient approach was developed by Klopman and Rosenkranz and implemented in the well-known program CASE [8][9][10][11][12]. It involved generation of all possible structural features (descriptors) of chemicals and statistics-based recognition of those determining mutagenic or carcinogenic activity.…”

Section: Introductionmentioning

confidence: 99%

“…This is much less than the corresponding values for pharmacological activity. To make the prediction of genotoxicity more efficient, the program Multicase invokes search for additional descriptors, called modulators [10,12]. These modulators can be both physicochemical traits and structural features.…”

Section: Introductionmentioning

confidence: 99%

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Use of Compound Structural Descriptors for Increasing the Efficiency of QSAR Study

et al. 2005

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A new concept of describing the dependence of the mutagenic activity of a chemical substance on its structure (QSAR analysis) is presented. It involves compound descriptors, which are combinations of unrelated fragments of molecular structure. Software has been developed to generate various structural fragments of molecules and their combinations (ensembles) and select compound descriptors of statistical significance for the biological activity of a chemical. By examples of univocal compound descriptors consisting of two structural fragments and present only in active or only in inactive compounds, it has been shown that the efficiency of QSAR study can be increased fourfold or more. The approach has been applied to a set of 105 compounds whose mutagenic effect on rodent germline cells is known.

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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Use of Compound Structural Descriptors for Increasing the Efficiency of QSAR Study

et al. 2005

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“…SAR models of these databases have also been described (41)(42)(43). We combined the individual projections derived from these different databases using Bayes' theorem, described previously (17,18) to yield a single prediction of carcinogenicity.…”

Section: Hpv Chemical Selectionmentioning

confidence: 99%

Estimating the extent of the health hazard posed by high-production volume chemicals.

Cunningham

Rosenkranz

2001

Environ Health Perspect

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We used structure-activity relationship modeling to estimate the number of toxic chemicals among the high-production volume (HPV) group. We selected 200 chemicals from among the HPV chemical list and predicted the potential of each for its ability to induce a variety of adverse effects including genotoxicity, carcinogenicity, developmental, and systemic toxicity. We found a significantly less than expected proportion of toxic chemicals among the HPV sample when compared to a reference set of 10,000 chemicals representative of the universe of chemicals.

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“…To classify an unknown compound back to a ligand or non-ligand category, rather than a probability of activity, the program identifies an optimal cut-off point that is able to separate ligand from non-ligand based on the model validation analysis [28]. The compound predicted with a value larger than the cut-off value is considered to be a ligand; otherwise it is considered a non-ligand.…”

Section: Predicting Activitymentioning

confidence: 99%

Structure-activity relationship model for estrogen receptor ligands.

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Identification of structural features and associated mechanisms of action for carcinogens in rats

Cited by 52 publications

References 23 publications

Use of Compound Structural Descriptors for Increasing the Efficiency of QSAR Study

Use of Compound Structural Descriptors for Increasing the Efficiency of QSAR Study

Estimating the extent of the health hazard posed by high-production volume chemicals.

Structure-activity relationship model for estrogen receptor ligands.

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