Relating chemical activity to structure: An examination of ILP successes View Full Text


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Article Info

DATE

1995-12

AUTHORS

Ross D. King, Michael J. E. Sternberg, Ashwin Srinivasan

ABSTRACT

Problems concerned with learning the relationships between molecular structure and activity have been important test-beds for Inductive Logic programming (ILP) systems. In this paper we examine these applications and empirically evaluate the extent to which a first-order representation was required. We compared ILP theories with those constructed using standard linear regression and a decision-tree learner on a series of progressively more difficult problems. When a propositional encoding is feasible for the feature-based algorithms, we show that such algorithms are capable of matching the predictive accuracies of an ILP theory. However, as the complexity of the compounds considered increased, propositional encodings becomes intractable. In such cases, our results show that ILP programs can still continue to construct accurate, understandable theories. Based on this evidence, we propose future work to realise fully the potential of ILP in structure-activity problem. More... »

PAGES

411-433

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf03037232

DOI

http://dx.doi.org/10.1007/bf03037232

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001934243


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