Searching by similarity is a basic necessity in pharmaceutical research and myriad of different approaches are available. In this webinar we take a closer look at the FTrees method. It is based on a topological pharmacophore description of the molecule (the Feature Tree) and an algorithm that calculates the similarity based on an optimal mapping of two Feature Trees. We quickly introduce the science and technology at work behind the scenes and then focus on the discussion of two example applications in the context of drug discovery.

The first example will cover the results obtained in several retrospective virtual screenings (VS) carried out by applying FTrees and standard 2D fingerprint-based similarity calculations. The aim of this study was to assess the potential of these methods to prioritise active compounds. In a follow up study, we then tested a new option in FTrees, namely the ability to nominate more and less important features on a query molecule, on the basis of the same benchmark data.

In the second example, a comparable analysis was performed in a prospective VS of a challenging drug discovery project. While both methods unfortunately failed to identify new active compounds, there was very limited overlap in the compounds selected by FTrees and 2D-fingeprints, suggesting that FTrees and 2D fingerprints may be considered as complementary approaches to define compound similarity.