The application of structure-activity relationships to the prediction of the mutagenic activity of chemicals.

Prediction of mutagenicity by computer is now routinely used in research and by regulatory authorities. Broadly, two different approaches are in wide use. The first is based on statistical analysis of data to find patterns associated with mutagenic activity. The resultant models are generally termed quantitative structure-activity relationships (QSAR). The second is based on capturing human knowledge about the causes of mutagenicity and applying it in ways that mimic human reasoning. These systems are generally called knowledge-based system. Other methods for finding patterns in data, such as the application of neural networks, are in use but less widely so.

[1]  S Dimitrov,et al.  Probabilistic assessment of biodegradability based on metabolic pathways: CATABOL System , 2002, SAR and QSAR in environmental research.

[2]  Joyce J. Kaufman,et al.  Prediction of toxicology and pharmacology based on model toxicophores and pharmacophores using the new TOX‐MATCH–PHARM‐MATCH program , 2009 .

[3]  Hao Zhu,et al.  ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases , 2004, J. Chem. Inf. Model..

[4]  K. Enslein,et al.  Use of SAR in computer-assited prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program , 1994 .

[5]  Ferenc Darvas,et al.  Metabolexpert: An Expert System for Predicting Metabolism of Substances , 1987 .

[6]  Y T Woo,et al.  Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals. , 1995, Toxicology letters.

[7]  C Helma,et al.  Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study , 2006, SAR and QSAR in environmental research.

[8]  Cancer Risk Reduction Through Mechanism-Based Molecular Design of Chemicals , 1996 .

[9]  G. Klopman.  Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .

[10]  J E Ridings,et al.  Computer prediction of possible toxic action from chemical structure: an update on the DEREK system. , 1996, Toxicology.

[11]  David J. Livingstone,et al.  Data Analysis for Chemists: Applications to QSAR and Chemical Product Design , 1996 .

[12]  Luc De Raedt,et al.  Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds , 2004, J. Chem. Inf. Model..

[13]  Gilles Klopman,et al.  META. 1. A Program for the Evaluation of Metabolic Transformation of Chemicals , 1994, J. Chem. Inf. Comput. Sci..

[14]  Philip Judson,et al.  Predicting Drug Metabolism – An Evaluation of the Expert System METEOR , 2005, Chemistry & biodiversity.

[15]  Philip N. Judson,et al.  Using Argumentation for Absolute Reasoning about the Potential Toxicity of Chemicals , 2003, J. Chem. Inf. Comput. Sci..

[16]  Gilles Klopman,et al.  META. 2. A Dictionary Model of Mammalian Xenobiotic Metabolism , 1994, J. Chem. Inf. Comput. Sci..

[17]  C. Hansch,et al.  A NEW SUBSTITUENT CONSTANT, PI, DERIVED FROM PARTITION COEFFICIENTS , 1964 .

[18]  Anita Young,et al.  Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data , 2005, J. Chem. Inf. Model..

[19]  Yin-tak Woo,et al.  OncoLogic: A Mechanism-Based Expert System for Predicting the Carcinogenic Potential of Chemicals , 2005 .

[20]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[21]  Michael H. Abraham,et al.  Linear solvation energy relationships. 23. A comprehensive collection of the solvatochromic parameters, .pi.*, .alpha., and .beta., and some methods for simplifying the generalized solvatochromic equation , 1983 .

[22]  Ralph Kühne,et al.  Estimation of Compartmental Half‐lives of Organic Compounds – Structural Similarity versus EPI‐Suite , 2007 .

[23]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[24]  R. King,et al.  Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. , 1996, Environmental health perspectives.

[25]  D. Sanderson,et al.  Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System , 1991, Human & experimental toxicology.

[26]  P. N. Craig,et al.  Carcinogenesis: a predictive structure-activity model. , 1982, Journal of toxicology and environmental health.

[27]  P N Judson,et al.  Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. , 1999, SAR and QSAR in environmental research.

[28]  Philip N. Judson,et al.  A Comprehensive Approach to Argumentation , 2003, J. Chem. Inf. Comput. Sci..

[29]  Lemont B. Kier,et al.  The electrotopological state: structure information at the atomic level for molecular graphs , 1991, J. Chem. Inf. Comput. Sci..

[30]  John Fox,et al.  A LOGIC OF ARGUMENTATION FOR REASONING UNDER UNCERTAINTY , 1995, Comput. Intell..

[31]  R. Tennant,et al.  Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. , 1988, Mutation research.