Abstract
Current approaches using Artificial Intelligence techniques applied to chemistry use representations inherited from existing tools. These tools describe chemical compounds with a set of structure-activity relationship (SAR) descriptors because they were developed mainly for the task of drug design. We propose an ontology based on the chemical nomenclature as a way to capture the concepts commonly used by chemists in describing molecular structure of the compounds. In this paper we formally specify the concepts and relationships of the chemical nomenclature in a comprehensive ontology using a form of relational representation called feature terms. We also provide several examples of describing chemical compounds using this ontology and compare our proposal with other SAR based approaches.
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References
Armengol, E., Plaza, E.: Bottom-up induction of feature terms. Machine Learning 41, 259–294 (2000)
Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence Journal 89, 31–71 (1997)
Armengol, E., Plaza, E.: Relational case-based reasoning for carcinogenic activity prediction. Artificial Intelligence Review 20, 121–141 (2003)
Armengol, E., Plaza, E.: Lazy learning for predictive toxicology based on a chemical ontology. In: Dubitzky, W., Azuaje, F. (eds.) Artificial Intelligence Methods and Tools for Systems Biology, Kluwer Academic Press, Dordrecht (2004) (in press)
Katritzky, A., Petrukhin, R., Yang, H., Karelson, M.: CODESSA PRO. User’s Manual. University of Florida (2002)
Todeschini, R., Consonni, V.: Handbook of Molecular Descriptors. Methods and principles in Medicinal Chemistry. Wiley-VCH, Weinheim (2000)
Gonzalez, J., Holder, L., Cook, D.: Application of graph-based concept learning to the predictive toxicology domain. In: Proceedings of the Predictive Toxicology Challenge Workshop, Freiburg, Germany (2001)
Deshpande, M., Karypis, G.: Automated approaches for classifying structures. In: Proc. of the 2nd Workshop on Data Mining in Bioinformatics (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Armengol, E., Plaza, E. (2005). An Ontological Approach to Represent Molecular Structure Information. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_30
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DOI: https://doi.org/10.1007/11573067_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
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