| Abstract
| - A primary goal of 3D similarity searching is to find compounds with similar bioactivity to areference ligand but with different chemotypes, i.e., “scaffold hopping”. However, an adequatedescription of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3Dchemical descriptions in which clustering techniques traditionally used in data mining areexploited to create “fuzzy” molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one ormore active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for activestaken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks.Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled byFEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoicacid). Importantly, the method excels in comparison to commonly used 2D similarity methods(DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.
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