Knowledge Discovery in Biological and Chemical Domains

Abstract

The pharmaceutical industry is increasingly overwhelmed by large-volume-data. This is generated both internally as a side-effect of screening tests and combinatorial chemistry, as well as externally from sources such as the human genome project. The industry is predominantly knowledge-driven. For instance, knowledge is required within computational chemistry for pharmacophore identification, as well as for determining biological function using sequence analysis. From a computer science point of view, the knowledge requirements within the industry give higher emphasis to “knowing that” (declarative or descriptive knowledge) rather than “knowing how” (procedural or prescriptive knowledge). Mathematical logic has always been the preferred representation for declarative knowledge and thus knowledge discovery techniques are required which generate logical formulae from data. Inductive Logic Programming (ILP) [6,1] provides such an approach

This talk will review the results of the last few years’ academic pilot studies involving the application of ILP to the prediction of protein secondary structure [5,8,9], mutagenicity [4,7], structure activity [3], pharmacophore discovery [2] and protein fold analysis [10]. While predictive accuracy is the central performance measure of data analytical techniques which generate procedural knowledge (neural nets, decision trees, etc.), the performance of an ILP system is determined both by accuracy and degree of stereo-chemical insight provided