At this stage of the challenge, the goal is to identify primary hits. In the absence of prior ligand information for the target, we are aiming at exploiting the state-ofthe-art protein subpocket-fragment interactions for de novo design, via a 4-step workflow. In the first step, the sc-PDB database  (30,000 protein-ligand complexes in the 2022 archive) ligands are fragmented in their co-crystallized conformation with an interaction-aware fragmentation method . Then, the protein environment around those fragments (shape and pharmacophoric features referred to as subpockets)  are compared to the target binding site, aligned, and scored with a local subpocket comparison method (ProCare) which had the advantage to be visually interpretable [4-5]. Fragments originating from the most similar subpockets are positioned into the target binding site according to their subpocket alignment. In the second step, fragments are assembled into larger molecules by designing linkers with a 3D deep generative method (DeLinker) , resulting in a target-focused library. In the third step, molecules are filtered according to desirable physicochemical properties (drug-likeness, synthetic accessibility) and searched in available/on-demand commercial databases. In the final step, available compounds are assessed by docking and rescoring using both empirical scoring functions [7-8] and topological validated methods (IChem) . Identified hits from the first-round design will be grown using the above-described procedure, focusing on unexplored subpockets in the target protein cavity. The unique workflow proposed here has been validated in previous published and unpublished studies, enabling (i) off-target prediction of approved drugs supported by experimental validation (see reference 5), and (ii) design of a target-focused library among which molecules were similar to know inhibitors and new hits were identified and validated experimentally. References:  Desaphy, J.; Bret, G.; Rognan, D.; Kellenberger, E. Nucleic Acids Res. 2015, 43 (D1), D399–D404.  Desaphy, J.; Rognan, D.J. Chem. Inf. Model. 2014, 54 (7), 1908–1918.  Desaphy, J.; Azdimousa, K.; Kellenberger, E.; Rognan, D. J. Chem. Inf. Model. 2012, 52 (8), 2287–2299.  Eguida, M.; Rognan, D. J. Med. Chem. 2020, 63 (13), 7127–7142.  Eguida, M.; Rognan, D. J. Cheminform. 2021, 13 (1), 1–13.  Imrie, F.; Bradley, A. R.; van der Schaar, M.; Deane, C. M. J. Chem. Inf. Model. 2020, 60 (4), 1983–1995.  Korb, O.; Stützle, T.; Exner, T. E. J. Chem. Inf. Model. 2009, 49 (1), 84–96.  Reulecke, I.; Lange, G.; Albrecht, J.; Klein, R.; Rarey. hemMedChem 2008, 3 (6), 885–897  Da Silva, F.; Desaphy, J.; Rognan, D. ChemMedChem 2018, 13 (6), 507–510.
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