Abstract
In this paper we will illustrate the results of a machine learning application concerning drug design. Dynamic bias management, in this context, will be presented as a critical mechanism to deal with complex problems in which good representations are unavailable even to human experts. A number of domain-dependent and domain-independent operators which allow automatic bias adjustment will be discussed with the mechanisms used to decide when and how to vary bias. Finally, we will summarize the results that a system named FLEMING adopting these techniques has obtained on the domain of the inhibitors of the thermolysin enzyme.
Chapter PDF
References
Bolis, G., Di Pace, L., Fabrocini, F., A Machine Learning Approach to Computer Aided Molecular Design, in International Journal of Computer Aided Molecular Design, (forthcoming).
Drastal, G., Czako, G., Raatz, S., Induction in an Abstraction Space: A Form of Constructive Induction, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI, 1989.
Flann, N., Dietterich, T. G., Selecting Appropriate Representations for Learning from Examples, in Proceedings of the Fifth National Conference on Artificial Intelligence, AAAI, 1986.
Holmes, M. A., Matthews, B. W., in Biochemistry, 20, 1981.
Klopman, G. J., Bendale, R. D., Computer Automated Structure Evaluation (CASE): A Study of Inhibitors of the Thermolysin Enzyme, in J. Theor. Biol., 136, 1989.
Martin, Y. C., Quantitative Drug Design, New York, Marcel Dekker, 1978.
Matheus, C. J., Rendell, L. A., Constructive Induction on Decision Trees, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI, 1989.
Michalski, R. S., A Theory and Methodology of Inductive Learning, in Michalski, R. S., Carbonell, J. G., Mitchell, T. M., eds., Machine Learning: An Artificial Intelligence Approach, vol. I, Palo Alto, Tioga Publishing Company, 1983.
Mitchell, T. M., The Need for Biases in Learning Generalizations, Tech. Rep. CBM-TR-117, Computer Sc. Dep., Rutgers University, 1980.
Muggleton, S., Duce, an oracle based approach to constructive induction, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI, 1987.
Pagallo, G., Learning DNF by Decision Trees, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI, 1987.
Rendell, L., Cho, H., Empirical Learning as a Function of Concept Character, in Machine Learning, 5, 1990.
Rendell, L., Seshu, R., Tcheng, D., Layered Concept-Learning and Dynamically-Variable Bias Management, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI, 1987.
Russell, S. J., Grosof, N. B., Delarative Bias: An Overview, in Benjamin, P., ed., Proceedings of the Philips Workshop on Reformulation and Inductive Bias, Boston, Kluwer Academic, 1989.
Schlimmer, J. C., Incremental Adjustment of Representations in Learning, in Proceedings of the International Workshop on Machine Learning, 1987.
Utgoff, P. E., Shift of Bias for Inductive Concept Learning, in Michalski, R. S., Carbonell, J. G., Mitchell, T. M., eds., Machine Learning: An Artificial Intelligence Approach, Los Altos, Morgan Kaufmann Publishers, 1986.
Wrobel, S., Automatic Representation Adjustment in an Observational Discovery System, in Proceedings of the Third European Working Session on Learning, 1988.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Pace, L., Fabrocini, F., Bolis, G. (1991). Shift of bias in learning from drug compounds: The fleming project. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017039
Download citation
DOI: https://doi.org/10.1007/BFb0017039
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53816-5
Online ISBN: 978-3-540-46308-5
eBook Packages: Springer Book Archive