Abstract
Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structuralel ements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structural chemistry. This paper describes an experiment in which the ILP system Progolw as used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol’s structurally-oriented hypotheses are preferable to those of other machine learning algorithms
The results in this paper are published separately in [7,16]
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
J. Black. Drugs from emasculated hormones: the principle of syntopic antagonism. Bioscience Reports, 9(3), 1989. Published in Les Prix Nobel, 1988. Printed in Sweden by Nostedts Tryckeri, Stockholm.
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
W. Buntine. Ind package of machine learning algorithms. Technical Report 244-17, Research Institute for Advanced Computer Science, NASA Ames Research Center, Moffett Field, CA 94035, 1992.
A.K. Debnath, R.L Lopez de Compadre, G. Debnath, A.J. Schusterman, and C. Hansch. Structure-Activity Relationship of Mutagenic Aromatic and Heteroaromatic Nitro compounds. Correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry, 34(2):786–797, 1991.
P. Finn, S. Muggleton, D. Page, and A. Srinivasan. Pharmacophore Discovery using the Inductive Logic Programming system Progol Machine Learning, 30: 241–271, 1998.
C.W. Gear. Numerical Initial Value Problems is Ordinary Differential Equations. Prentice-Hall, Edgewood Cliffs, NJ, 1971.
R.D. King, S.H. Muggleton, A. Srinivasan, and M.J.E. Sternberg. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. of the National Academy of Sciences, 93:438–442, 1996.
J. McCarthy. Programs with commonsense. In Mechanisation of thought processes (v1). Her Majesty’s Stationery Office, London, 1959. Reprinted with an additional section in Semantic Information Processing.
R.S. Michalski. A theory and methodology of inductive learning. In R. Michalski, J. Carbonnel, and T. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 83–134. Tioga, Palo Alto, CA, 1983.
D. Michie. The superarticulacy phenomenon in the context of software manufacture. Proceedings of the Royal Society of London, A 405:185–212, 1986. Reprinted in: The Foundations of Artificial Intelligence: a Sourcebook (eds. D. Partridge and Y. Wilks), Cambridge University Press, 1990.
D. Michie, D.J. Spiegelhalter, and C.C. Taylor, editors. Machine Learning, Neural and Statistical classification. Ellis-Horwood, New York, 1994.
S. Muggleton. Inverse Entailment and Progol. New Gen. Comput., 13:245–286, 1995.
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20:629–679, 1994.
A. J. Owens and D. L. Filkin. Efficient training of the back-propagation network by solving a system of stiff ordinary differential equations. In Proceedings IEEE/INNS International Joint Conference of Neural Networks, pages 381–386, Washington DC, 1989.
G. Ryle. The Concept of Mind. Hutchinson, 1949.
A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Theories for mutagenicity: a study of first-order and feature based induction. Artificial Intelligence, 85:277–299, 1996.
D. Villemin, D. Cherqaoui, and J.M. Cense. Neural network studies: quantitative structure-activity relationship of mutagenic aromatic nitro compounds. J. Chim. Phys, 90:1505–1519, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muggleton, S., Srinivasan, A., King, R.D., Sternberg, M.J.E. (1998). Biochemical Knowledge Discovery Using Inductive Logic Programming. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_29
Download citation
DOI: https://doi.org/10.1007/3-540-49292-5_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65390-5
Online ISBN: 978-3-540-49292-4
eBook Packages: Springer Book Archive