STRUCTURE u DITERPENE ELUCIDATION FROM 1 3 C NMR SPECTRA WITH INDUCTIVE LOGIC PROGRAMMING

W e present a novel application of Inductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 1 3 C NMR spectra. Diterpenes are organic compounds of low molecular weight with a skeleton of 20 carbon atoms. T hey are of signicant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical e ectors. T he interpretation of diterpene 1 3 C NMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, speciically knowledge on peak patterns and chemical structures. Given a database of peak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. T he approaches used include rst-order inductive learning, relational instance based learning, induction of logical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.