Computer assisted structure-activity studies of chemical carcinogens. An N-nitroso compound data set

Chou, Jia‐Hong; Jurs, Peter C.

doi:10.1021/jm00193a008

Cited by 36 publications

(10 citation statements)

References 17 publications

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“…When simple classification or preselection of already existing compounds and not so much interpretation and design are in the foreground the "index" or classification methods outlined above are to be applied. Such methods have also been applied in the field of chemical hazards such as toxic effects (49,50) carcinogenicity (20,(49)(50)(51)(52), and mutagenicity (43,44,49) not without success, although the results thus far obtained more or less suffer from the deficiencies discussed earlier (e.g., extremely complex classifier). Considering the number of chemicals already in circulation and those to be expected from further syntheses, such analyses are to be regarded as indispensable tools to set priorities for biological testing.…”

Section: Discussionmentioning

confidence: 99%

Substructural QSAR approaches and topological pharmacophores.

Franke¹,

Huebel²,

Streich³

1985

Environ Health Perspect

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For large and diverse data sets, simple QSAR methods based on linear and additive models can no longer be applied. In such cases topological methods using descriptors directly derivable from two-dimensional chemical structures provide a useful alternative. The results of such analyses can be used for lead optimization, to guide biological testing and even aid in the design of novel compounds. Various types of topological descriptors and algorithms are briefly discussed. Which of those is to be selected depends on the objective of the investigation and the properties of the data set. Two new methods, LOGANA and LOCON, are discussed in some more detail. With the help of these methods, substructural patterns ("topological pharmacophores") characteristic of compounds possessing a certain biological property can be evaluated. Both methods are designed in such a way that full use can be made of the data handling capacity of computers while maintaining an optimal impact of the experience of the researcher. They are model-free and do not require any mathematical knowledge. While LOGANA deals with semiquantitative or even qualitative biological data, LOCON can be applied to activity data on a continuous scale. The basic procedure in both cases consists in the stepwise combination of substructural descriptors by the logical operations "and," "or" and "not." With a simple example the utility of the methods is demonstrated.

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Section: Discussionmentioning

confidence: 99%

Substructural QSAR approaches and topological pharmacophores.

Franke¹,

Huebel²,

Streich³

1985

Environ Health Perspect

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“…ADAPT studies have been performed on antitumor agents (11), carcinogenic substances (8,9,12,13), and olfactory stimulants (10). This system conveniently integrates the many steps required for a complete SAR study not only using PR but also using a variety of statistical techniques.…”

Section: Methodology and Problems Involved In Studies Applying Linearmentioning

confidence: 99%

Computer-Assisted Studies of Molecular Structure and Genotoxic Activity by Pattern Recognition Techniques

Stouch¹,

Jurs²

1985

Environmental Health Perspectives

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. The National Institute of Environmental Health Sciences (NIEHS) and Brogan & Partners are collaborating with JSTOR to digitize, preserve and extend access to Environmental Health Perspectives.Often a compound's biological activity is determined by complex relationships between its structural components. Such a relationship often can only be adequately described and exploited by multivariate structure-activity relationship (SAR) studies that can deal with many variables simultaneously. Pattern recognition (PR) is a multivariate technique that is well suited for the qualitative, active-inactive, data that is often supplied by biological assays. PR studies of compounds of known activity can yield information that will allow the prediction of the activity of untested compounds. ADAPT is a computerized system that was developed for such PR-SAR studies. A general introduction to this field is presented and the methodology used for such a study is described in the context of an actual study of mutagenic compounds. The data requirements, descriptor generation, and the details of a PR study are discussed. In addition, the example study was chosen to highlight the problems that may occur if a study is not well formulated and carefully executed. Current work and future plans for computerized mutagen screening are discussed. FIGURE 1. Boiling point vs. melting point for some simple aldehydes (A) and ketones (K). Note that the two classes cluster in separate regions of the plot. Z is a compound of unknown classification. eral assumptions: (1) The activity and its variation can be explained by variations within the structures. (2) The structures can be sufficiently described by numerical indices (descriptors). (3) Pattern recognition techniques can be used to discover a relationship between the descriptors and the activity. (4) This relationship can be extrapolated to untested compounds.Each compound in a study is referred to as an observation or pattern and each structural feature or experimental property is referred to as a variable or descriptor. Simple problems can be viewed graphically as in Figure 1. This is a plot of several aldehydes (A) and ketones (K) as represented by their melting points and boiling points. In the "space" of these two physical parameters, the aldehydes cluster in a different region of this "two-space" than the ketones. This differential clustering is the basis for pattern recognition. If a new compound (Z) is plotted in this same space, the likelihood is high that it will belong to the same class as neighboring patterns, in this case, aldehydes. This is a very simple example of cluster analysis. Techniques that generate discriminant functions also rely on this clus...

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“…The Jurs group has been one of the more active groups in the field of SAR studies on mutagens and carcinogens. One of the Jurs group's earliest studies [Chou and Jurs, 1979b] was that done on 153 compounds, 118 carcinogens and 35 noncarcinogens, containing the N-nitroso group. As discussed above, the ADAPT program is a pattern recognition method.…”

Section: Mutagenic and Carcinogenic Studies Using Adaptmentioning

confidence: 99%

Structure‐activity relationships (SARs) among mutagens and carcinogens: A review

1986

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This review is an introduction to methods for evaluating structure-activity relationships (SARs), and, in particular, to those methods that have been applied to study mutagenicity and carcinogenicity. A brief history and some background material on the earliest attempts to correlate molecular structure and biological activity are included. Most of the discussion focuses on modern methods utilizing extrathermodynamic and physical property variables such as the Hansch method and SIMCA, and approaches based on molecular connectivity such as the ADAPT, CASE, and Enslein methods. In general, the latter class is potentially the most useful in the study of the large and structurally diverse databases so often encountered in the study of mutagenicity and carcinogenicity. They also are not very sensitive to lab-to-lab variances in reported activities and outright misclassifications in activities of some compounds. This is chiefly because the statistical treatments used in these methods tend to dilute the importance of outliers. The methods using physicochemical and extrathermodynamic variables are especially important in relatively small, congeneric databases and can help fine-tune the role of physicochemical properties in mechanistic hypotheses. All of the above methods have been used to look at mutagenicity and carcinogenicity and some of the results reported in the literature are reviewed here. As far as specific methods go, ADAPT, CASE, SIMCA and the Enslein approach all seem to have similar classification powers (in the range of 75-95%), depending very much on the database studied. The emphasis in this review is on showing that the use of these computer-aided storage, retrieval and analysis techniques is a timely approach to predicting and even understanding the toxicity of environmental substances. However, each of the methods discussed is still under development, and their potential usefulness for predictive purposes is still being explored.

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Computer assisted structure-activity studies of chemical carcinogens. An N-nitroso compound data set

Cited by 36 publications

References 17 publications

Substructural QSAR approaches and topological pharmacophores.

Substructural QSAR approaches and topological pharmacophores.

Computer-Assisted Studies of Molecular Structure and Genotoxic Activity by Pattern Recognition Techniques

Structure‐activity relationships (SARs) among mutagens and carcinogens: A review

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