In drug discovery the molecular docking approaches have been successfully used to screen large databases of molecules in silico to identify small molecule ligands for protein targets. However, there are significant challenges in accurately screening millions of compounds given the large computational cost. Based on the programmable growth engine of CONTOURTM technology, we developed a constraint-biased in silico high-throughput screening method accelerated by a novel tree-topology mapping methodology. The growth-based docking algorithm was used to screen the ZINC database to identify novel molecules that bind to 11�-hydroxysteroid dehydrogenase type 1 (11� HSD-1) and competitively inhibit its enzymatic activity. The docking site in the enzyme was identified by key hydrogen-bonds to the catalytic residues, which was then used as a constraint to filter molecules from the database that contain a complementary functional group able to form key hydrogen bonds. Starting with optimal binding geometries of hydrogen bonding functional groups, the docked molecules were mapped onto trees with the functional groups as the roots. Then the growth algorithm generated the rest of the molecules one fragment at a time into the protein binding pocket by sampling conformations as defined in the tree topologies in order to avoid sampling redundancy. The details of the algorithm, the database processing, and the screening effort which identified a novel, 30 nM 11� HSD-1 inhibitor will be presented.

Molecular growth algorithm based in silico high-throughput screening approach in CONTOUR™ used to identify inhibitors of 11β-hydroxysteroid dehydrogenase type 1

243rd National Meeting (2012)

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