Due to the lack of available screening data for molecules targeting the WD40 repeat, we will start by searching the Enamine Make-On-Demand library with our own novel evolutionary optimization algorithm RosettaLigandEvolution (LigEvol) and ligand docking with RosettaLigand. Both are part of the Rosetta software suite and developed by us. RosettaLigand optimizes ligandprotein complexes with complete small molecule, side chain, and backbone flexibility to capture all aspects for determining binding conformation and affinity. LigEvol reliably optimizes ligands through fragment search in combinatorial libraries like Enamine and identifies local molecule minima. Not only does it detect promising compounds with ease, but it also strictly remains inside Enamine make-on-demand space. We aim at sampling 100,000 molecules and expect, based on previous experiments, to derive 1,000 potential hit candidates. Next, we will seed LigEvol with promising compounds to further explore the chemical space close to areas of high interest and train a QSAR and an applicability domain (AD) model with our own BioChemicalLibrary (BCL) software on the predictions from RosettaLigand. The QSAR model allows us to screen the entire database to identify areas of chemical space of higher priority, and we can reiterate LigEvol while seeding it with reagents that are closest to areas of interest. At this point, we expect to have a large number of diverse compounds with a high predicted affinity, covering most if not all of the chemical space of interest. The final selection will be picked through the appliance of multiple BCL filters for drug-likeness, solubility, and more. We will ensure a high diversity of scaffolds through clustering. Finally, our medicinal chemists will manually inspect the compounds and prioritize 100 candidate molecules, which can be excellent starting points for hit optimization, for testing.
Rosetta Suite, Biology and Chemistry Library (BCL), PyMOL, Python– RDKit and other free libraries