Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra

We present a novel application of ILP to the problem of diterpene structure elucidation from 13 C NMR spectra. Diterpenes are organic compounds of low molecular weight that are based on a skeleton of 20 carbon atoms. They are of signiicant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical eeectors. The structure elucidation of diterpenes based on 13 C NMR spectra is usually done manually by human experts with specialized background knowledge on peak patterns and chemical structures. In the process, each of the 20 skeletal atoms is assigned an atom number that corresponds to its proper place in the skeleton and the diterpene is classiied into one of the possible skeleton types. We address the problem of learning classiication rules from a database of peak patterns for diterpenes with known structure. Recently, propositional learning was successfully applied to learn classiication rules from spectra with assigned atom numbers. As the assignment of atom numbers is a diicult process in itself (and possibly indistinguishable from the clas-siication process), we apply ILP, i.e., relational learning, to the problem of classifying spectra without assigned atom numbers.