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Auxins are defined mainly by a set of physiological actions, but the structure-effect relationship still is based on chemical intuition. Currently a well-defined auxin molecular structure is not available. The existence of different auxin binding proteins and mechanisms of auxin action, the wide diversity of the auxin molecules, and the pleiotropic effects of auxin imply a completely different mechanism as described for the animal hormone concept. Here, we present a computational approach dealing with semiempirical optimizations of the auxin molecules themselves, which represent a number of about 250 different chemical structures. Our approach uses molecular quantum similarity measures and additional quantum variables for the analysis of auxin-like molecules. The finding of similarities in molecules by focusing basically on their electron structure results in new insights in the relationship of the different auxin groups. Additional statistical analysis allows the identification of relationships between similarity groups and their biological activity, respectively. It is postulated that the auxin-like molecular recognition depends more on specific molecular assembling states than on a specific ring system or side chain.

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