Theories for mutagenicity: a study in first-order and feature-based induction

Journal article

A classic problem from chemistry is used to test a conjecture that in domains for which data are most naturally represented by graphs, theories constructed with inductive logic programming (ILP) will significantly outperform those using simpler feature-based methods. One area that has long been associated with graph-based or structural representation and reasoning is organic chemistry. In this field, we consider the problem of predicting the mutagenic activity of small molecules: a property that is related to carcinogenicity, and an important consideration in developing less hazardous drugs. By providing an ILP system with progressively more structural information concerning the molecules, we compare the predictive power of the logical theories constructed against benchmarks set by regression, neural, and tree-based methods.

Authors: Srinivasan, A; Muggleton, S; Sternberg, M; King, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Artificial Intelligence
• Volume: 85
• Issue: 1–2
• Pages: 277–299
• Publication date: 1996-08-01
• Edition: Publisher's version
• DOI: 10.1016/0004-3702(95)00122-0
• ISSN: 0004-3702
• Language: English
• Keywords: inductive logic programming; mutagenicity
• Subjects: Applications and algorithms; Computational chemistry; Organic chemistry