Feature construction with inductive logic programming: A study of quantitative predictions of biological activity by structural attributes

Abstract

Recently, computer programs developed within the field of Inductive Logic Programming (ILP) have received some attention for their ability to construct restricted first-order logic solutions using problemspecific background knowledge. Prominent applications of such programs have been concerned with determining “structure-activity” relationships in the areas of molecular biology and chemistry. Typically the task here is to predict the “activity” of a compound, like toxicity, from its chemical structure. Research in the area shows that: (a) ILP programs have been restricted to qualitative predictions of activity (“high”, “low” etc.); (b) When appropriate attributes are available, ILP programs have not been able to better the performance of standard quantitative analysis techniques like linear regression. However ILP programs perform creditably when such attributes are unavailable; and (c) When both are applicable, ILP programs are usually slower than their propositional counterparts. This paper examines the use of ILP programs, not for obtaining theories complete for the sample, but as a method of “discovering” new attributes. These could then be used by methods like linear regression, thus allowing for quantitative predictions and the ability to use structural information as background knowledge. Using structure-activity tasks as a test-bed the utility of ILP programs in constructing new features was evaluated by examining the prediction of chemical activity using linear regression, with and without the aid of ILP learnt logical attributes. In three out of the five datasets examined the addition of ILP attributes produced statistically better results (P i0.01). In addition six important structural features that have escaped the attention of the expert chemists were discovered.