Combination of ethnopharmacological knowhow with modern in silico tools

Computational methods are valuable tools in current drug discovery and development processes. They aim at analyzing, understanding, and predicting the bioactivity of a compound with respect to a specific biological target, and have been applied successfully in medicinal chemistry.

Their application in natural product research is however affected with some challenges, such as limited availability of high quality natural product databases, often restricted or laborious access to individual compounds for testing, and lack of chemoinformatics experience with secondary metabolites. This asks for a sensible application of data mining tools in this prospering field of lead finding from nature.

The hyphenation of in silico strategies with knowledge from ethnopharmacology offers a unique opportunity to benefit from a combined theoretical and empirical approach. Herbal remedies, which are used since centuries, represent a particularly promising resource for drug leads. These often undefined multicomponent mixtures play a dominant role in healthcare worldwide with increasing information on traditional and biomedical uses and with links between multi-target molecular pharmacology and clinical medicine.

This presentation describes strategies how to integrate computational strategies in pharmacognostic workflows to disclose promising bioactive compounds or hidden information about affected pharmacological targets. I will focus specifically on the expectations, possibilities and limits when using computational tools in phytochemistry, and present some recent examples of these highly complementary, but synergistic approaches, viz. in silico – in traditio.

Keywords: computational methods, in silico, ethnopharmacology, drug discovery