Predicting Chemical Carcinogenesis Using Structural Information Only
This paper reports on the application of the Strongly Typed Evolutionary Programming System (STEPS) to the PTE2 challenge, which consists of predicting the carcinogenic activity of chemical compounds from their molecular structure and the outcomes of a number of laboratory analyses. Most contestants so far have relied heavily on results of short term toxicity (STT) assays. Using both types of information made available, most models incorporate attributes that make them strongly dependent on STT results. Although such models may prove to be accurate and informative, the use of toxicological information requires time cost and in some cases substantial utilisation of laboratory animals. If toxicological information only makes explicit, properties implicit in the molecular structure of chemicals, then provided a sufficiently expressive representation language, accurate solutions may be obtained from the structural information only. Such solutions may offer more tangible insight into the mechanistic paths and features that govern chemical toxicity as well as prediction based on virtual chemistry for the universe of compounds.
Unable to display preview. Download preview PDF.
- 1.Bahler, D.R., Bristol, D.W.: The induction of rules for predicting chemical carcinogenesis in rodents. In: Intelligent Systems for Molecular Biology, pp. 29–37. AAAI/MIT Press, Cambridge (1993)Google Scholar
- 2.Bristol, D.W., Wachsman, J.T., Greenwell, A.: The NIEHS predictive toxicology evaluation project. Environmental Health Perspectives 3, 1001–1010 (1996)Google Scholar
- 4.Kennedy, C.J., Giraud-Carrier, C.: An evolutionary approach to concept lear- ning with structured data. In: Proceedings of the Fourth International Conference on Artificial Neural Networks and Genetic Algorithms. Springer, Heidelberg (1999)Google Scholar
- 5.King, R., Muggleton, S., Srinivasan, A., Sternberg, M.: Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity in inductive logic programming. Proceedings of the National Academy of Sciences 93, 438–442 (1996)CrossRefGoogle Scholar
- 9.Srinivasan, A., King, R.: Carcinogenisis predictions using ILP. In: Proceedings of the Seventh Inductive Logic Programming Workshop. LNCS (LNAI). Springer, Heidelberg (1997)Google Scholar
- 10.Srinivasan, A., King, R., Muggleton, S.: The role of background knowledge: Using a problem from chemistry to examine the performance of an ILP program. In: Intelligent Data Analysis in Medicine and Pharmacology. Kluwer Academic Press, Dordrecht (1996)Google Scholar
- 11.Srinivasan, A., King, R.D., Muggleton, S.H., Sternberg, M.: The predictive toxicology evaluation challenge. In: Proceedings of the Fifteenth International Joint Conference Artificial Intelligence (IJCAI 1997). Morgan-Kaufmann, San Francisco (1997)Google Scholar
- 12.Srinivasan, A., Muggleton, S., King, R., Sternberg, M.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Proceedings of Fourth Inductive Logic Programming Workshop. Gesellschaft für Mathematik und Datenverarbeitung MBH (1994)Google Scholar
- 13.van Hemert, J.I., Eiben, A.E.: Comparison of the SAW-ing evolutionary algorithm and the grouping genetic algorithm for graph coloring. Technical Report TR-97-14, Leiden University (1997)Google Scholar