Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

Abstract

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.