Diterpene Structure Elucidation from 13C NMR-Spectra with Machine Learning

Abstract

Diterpenes are organic compounds of low molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical effecters. The interpretation of diterpene C-13 NMR-spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, more specifically knowledge on peak patterns and chemical structures. Given a database of peak patterns for diterpenes with known structure, we apply machine learning approaches to discover correlations between peak patterns and chemical structure. Backpropagation neural networks, nearest neighbor classification and decision tree induction are applied, as well as approaches from the field of inductive logic programming. Performance close to the one of domain experts is achieved, which suffices for practical use.