Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network

Journal of Chemical Information and Computer Sciences

Volumn 39, Issue 6, 1999, Pages 1076-1080

  Gini, Giuseppina ^a   Lorenzini, Marco ^a   Benfenati, Emilio ^b   Grasso, Paola ^b   Bruschi, Maurizio ^b  

^a POLITECNICO DI MILANO   (Italy)

^b MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CARCINOGEN; NITROGEN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CARCINOGEN TESTING; CHEMICAL MODEL; CHEMISTRY; STRUCTURE ACTIVITY RELATION;

CARCINOGENICITY TESTS; CARCINOGENS; MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); NITROGEN; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 0033217369     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci9903096     Document Type: Article

References (18)

1. Omenn, G. S. Assessing the Risk Assessment Paradigm. Toxicology 1995, 102, 23-28.
2. Benfenati, E.; Gini, G. Computational Predictive Programs (Expert Systems) in Toxicology. Toxicology 1997, 119, 213-225.
3. Dearden, J. C.; Barratt, M. D.; Benigni, R.; Bristol, D. W.; Combes, R. D.; Cronin, M. T. D.; Judson, P. N.; Payne, M. P.; Richard, A. M.; Tichy, M.; Worth, A. P.; Yourick, J. J. The Development and Validation of Expert Systems for Predicting Toxicity. ATLA 1997, 25, 223-252.
4. Gini, G. C.; Katritzky, A. R. Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AAAI Press: Menlo Park, CA, 1999; pp 1-155.
5. Karelson, M.; Maran, U.; Wang, Y.; Katritzky, A. R. QSPR and QSAR Models Derived with CODESSA Multipurpose Statistical Analysis Software. In Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AMI 1999 Spring Symposium Series; Gini, G. C., Katritzky, A. R., Eds.; AAAI Press: Menlo Park, CA, 1999; pp 45-48.
6. Villemin, D.; Cherqaoui, D.; Mesbash, A. Predicting Carcinogenicity of Polycyclic Aromatic Hydrocarbons from Back-Propagation Neural Network. J. Chem. Inf. Comput. Sci. 1994, 34, 1288-1293.
7. Benigni, R.; Richard, A. M. QSARS of Mutagens and Carcinogens: Two Case Studies Illustrating Problems in the Construction of Models for Noncongeneric Chemicals. Mutat. Res. 1996, 371, 29-46.
8. Vracko, M. A Study of Structure-Carcinogenic Potency Relationship with Artificial Neural Networks. The Using of Descriptors Related to Geometrical and Electronic Structures. J. Chem. Inf. Comput. Sci. 1997, 37, 1037-1043.
9. Gold, L. S.; Slone, T. H.; Manley, N. B.; Backman Garfinkel, G.; Hudes, E. S.; Rohrbach, L.; Ames, B. N. The Carcinogenic Potency Database: Analyses of 4000 Chronic Animal Cancer Experiments Published in the General Literature and by the U.S. National Cancer Institute/National Toxicology Program. Environ. Health Perspect. 1991, 96, 11-15.
10. Benfenati, E.; Tichy, M.; Malvè, L.; Grasso, P.; Gini, G. Expert Systems for Toxicity Prediction Based on Fragment Recognition: Evaluation of a Commercial System and Improved Approaches; American Chemical Society Meeting, Las Vegas, NV, Sep 8-12, 1997; Abstracts of paper, COMP 136.
11. Anguita, D. Matrix Back Propagation v 1.1: User's Manual; 1993. Available through anonymous ftp at risc6000.dibe.unige.it.
12. Miao, X.; Azimi-Sadjadi, M. R.; Tina, B.; Dubey, A. C.; Witherspoon, N. H. Detection of Mines and Minelike Targets Using Principal Component and Neural-Network Methods. IEEE Trans. Neural Networks 1998, 9, 454-463.
13. Ventura, S.; Silva, M.; Pérez-Bendito, D.; Hervás, C. Computational Neural Networks in Conjunction with Principal Component Analysis for Resolving Highly Nonlinear Kinetics. J. Chem. Inf. Comput. Sci. 1997, 37, 287-291.
14. Sussman, N. B.; Marina, O. T.; Claycamp, H. G.; Grant, S. G.; Rosenkranz, H. S. The Utility of Multiple Random Sampling in the Development of SAR Models. In Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AAAI 1999 Spring Symposium Series; Gini, G. C., Katritzky, A. R., Eds.; AAAI Press: Menlo Park, CA, 1999; pp 45-48.
15. Gini, G.; Lorenzini, M.; Vittore, A.; Benfenati, E.; Grasso, P. Some Results for the Prediction of Carcinogenicity Using Hybrid Systems. In Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AAAI 1999 Spring Symposium Series; Gini, G. C., Katritzky, A. R., Eds.; AAAI Press: Menlo Park, CA, 1999; pp 139-143.
16. Helma, C.; Gottmann, E.; Kramer, S.; Pfahringer, B. Data Quality Issues in Toxicological Knowledge Discovery. In Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AAAI 1999 Spring Symposium Series; Gini, G. C., Katritzky, A. R., Eds.; AAAI Press: Menlo Park, CA, 1999; pp 8-11.
17. Bahler, D.; Bristol, D. W. Prediction of Chemical Carcinogenicity in Rodents by Machine Learning of Decision Trees and Rule Sets. In Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools; AAAI 1999 Spring Symposium Series; Gini, G. C., Katritzky, A. R., Eds.; AAAI Press: Menlo Park, CA, 1999; pp 74-77.
18. Gini, G.; Testaguzza, V.; Benfenati, E.; Todeschini, R. HyTEx (Hybrid Toxicology Expert System): Architecture and Implementation of a Multi-domain Hybrid Expert System for Toxicology. Chemom. Intell. Lab. Syst. 1998, 43, 135-145.