Biochemical Knowledge Discovery Using Inductive Logic Programming

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.

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