Using Inductive Logic Programming to construct Structure-Activity Relationships
Ashwin Srinivasan, Ross D. King · 1999
The existence and rapid growth of chemical databases have brought into focus the utility of methods that can assist the discovery of predictive patterns in data, and communicating them in a manner designed to provoke insight. This has turned attention to machine learning techniques capable of extracting "symbolic" descriptions from data. At the cutting-edge of such techniques is Inductive Logic Programming (ILP). Given a set of observations and background knowledge encoded as a set of logical descriptions, an ILP system attempts to construct explanations for the observations. The explanations are in the same language as the observations and background knowledge -- usually a subset of first-order logic. The use of first-order logic contrasts with algorithms like decision-trees, and neural networks which employ simple propositional logic representations. The increased representation power along with the flexibility to include background knowledge -- which can even inclu...