Relationship between Molecular Connectivity and Carcinogenic Activity: A Confirmation with a New Software Program Based on Graph Theory

Malacarne, Davide; Pesenti, Raffaele; Paolucci, Massimo; Parodi, Silvio

doi:10.2307/3431444

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…An implementation of MULTICASE, tailored specifically on the issue of predicting the carcinogenicity of pharmaceutical drugs, is in ref . Independent software implementations of the CASE technology were developed by Parodi and collaborators. − References to PROGOL are 136−139.…”

Section: 1 Statistically Based Approaches For Noncongeneric Datasetsmentioning

confidence: 99%

Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

2005

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Section: 1 Statistically Based Approaches For Noncongeneric Datasetsmentioning

confidence: 99%

Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

2005

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“…Tbe idea of using linear fragments as features of chemical structures was developed in the l98O's by Gilles Klopman and his CASE/M-CASE system [10] and subsequently re-implemented by Malacame el al. [11]. The following data mining algorithm is able to identify linear fragments very efficiently in databases of molecular structures.…”

Section: Linear Fragmentsmentioning

confidence: 99%

Data Mining and Knowledge Discovery in Predictive Toxicology

Helma¹

2004

SAR and QSAR in Environmental Research

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This article describes the knowledge discovery process in predictive toxicology. This process consists of five major steps (i) feature calculation, (ii) feature selection, (iii) model induction, (iv) model validation and (v) interpretation of predictions and models. Data mining is a part of the knowledge discovery process and consists of the application of data analysis and discovery algorithms, which can be useful in all of the above steps. A brief review of suitable algorithms and their advantages and disadvantages is given for each knowledge discovery step, followed by a more detailed description of a problem-specific implementation of the lazar prediction system.

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“…Considering their 2D structure, substructures can have different degrees of complexity, namely linear (chains of atoms), tree-shaped (with branches, but without cycles) and substructures of any shape. Various programs exist that can mine moderately [18,19] and considerably [8] sized toxicity datasets for all linear substructures within acceptable time limits. The number of distinct nonlinear substructures (with branches and cycles) in the same dataset, on the other hand, is several orders of magnitude higher.…”

Section: Substructures In Standard Chemical Notationmentioning

confidence: 99%

Artificial Intelligence and Data Mining for Toxicity Prediction

Helma¹,

Kazius²

2006

CAD

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Tools for artificial intelligence and data mining can derive (Quantitative) Structure-Activity Relationships ((Q)SARs) for toxicity in an objective and reproducible manner. This review provides a conceptual description of the most important data mining algorithms for the identification of chemical features and the extraction of relationships between these descriptors and toxic activities. We will discuss the compliance of these techniques with the OECD guidelines for (Q)SAR requirements as well as performance implications. Special emphasis will be given to validation procedures for (Q)SAR models.

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Relationship between Molecular Connectivity and Carcinogenic Activity: A Confirmation with a New Software Program Based on Graph Theory

Cited by 7 publications

References 25 publications

Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

Data Mining and Knowledge Discovery in Predictive Toxicology

Artificial Intelligence and Data Mining for Toxicity Prediction

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