Using inductive logic programming to discover knowledge hidden in chemical data

Bryant, CH; Adam, AE; Taylor, DR; Rowe, RC

Abstract

This paper demonstrates how general purpose tools from the field of Inductive Logic Programming (ILP) can be applied to analytical chemistry. As far as these authors are aware, this is the first published work to describe the application of the ILP tool Golem to separation science. An outline of the theory of ILP is given, together with a description of Golem and previous applications of ILP. The advantages of ILP over classical machine induction techniques, such as the Top-Down-Induction-of-Decision-Tree family, are explained. A case-study is then presented in which Golem is used to induce rules which predict, with a high accuracy (82%), whether each of a series of attempted separations succeed or fail. The separation data was obtained from published work on the attempted separation of a series of 3-substituted phthalide enantiomer pairs on (R)-N-(3,5-dinitrobenzoyl)-phenylglycine.

Citation

Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1998). Using inductive logic programming to discover knowledge hidden in chemical data. Chemometrics and Intelligent Laboratory Systems, 36(2), 111-123. https://doi.org/10.1016/S0169-7439%2897%2900023-3

• Journal Article Type: Article
• Online Publication Date: Mar 26, 1998
• Publication Date: Mar 26, 1998
• Deposit Date: Feb 17, 2009
• Publicly Available Date: Feb 17, 2009
• Journal: Chemometrics and Intelligent Laboratory Systems
• Print ISSN: 0169-7439
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Issue: 2
• Pages: 111-123
• DOI: https://doi.org/10.1016/S0169-7439%2897%2900023-3
• Keywords: inductive logic programming, golem
• Publisher URL: http://dx.doi.org/10.1016/S0169-7439(97)00023-3