Our proposal is to link a set of fragments co-crystallized with nsp3, using either a deep generative model or a knowledge-based linker database, to afford drug-like molecules spanning at least two subpockets of the target. In a first step, 186 co-crystallized fragments (Schuller et al., Sci Adv. 2021) occupying one of 5 possible subpockets (adenine site, oxyanion site, upper site, lower site, pyrophosphate loop site) will be placed in the atomic coordinate frame of a reference nsp3 structure (PDB ID 6W02). For each fragment, possible connectable atoms (with other fragments) will be selected manually and tagged. The second step will consist in linking fragment pairs according to their location. Only fragments occupying two neigbouring subpockets (adenine-oxyanion, adenine-upper, adenine-pyrophosphate, oxyanion-pyrophosphate, oxyanion-lower, pyrophosphate-lower) will be allowed to be connected to yield fully enumerated compounds. Two linking approaches will be pursued using either a deep generative model (Imrie et al. J Chem Inf Model, 2020) or a novel in-house organic chemistry-driven protocol enabling to link remote fragments directly the target 3D coordinate frame by a set of 5854 3D linkers derived from PDB fragmented-ligands (Eguida et al. J Med Chem, 2022). This new approach is followed by a quick minimization inside of the target-cavity (MMFF94 force field) to relax the structure and discard linkers leading to a modification of the seed fragment locations. All proposals will be docked to the reference nsp3 structure to ensure that at least one docking pose is able to locate the starting two fragments close to their X-ray coordinates (rmsd < 2 Å). After filtering for physicochemical, drug-likeness and synthetic accessibility properties (Eguida et al. J Med Chem, 2022), remaining hits will be searched for maximum common substructure similarity to REAL database compounds (Enamine Ltd, Kyiv, Ukraine). Up to 100 of the most similar compounds will be purchased.

The proposed approach relies on a unique workflow able to generate 3D molecules into a target-cavity by connecting prepositioned fragments of known binding mode, using a set of standard organic chemistry reactions (e.g. amide bond formation, Suzuki coupling, reductive amination). According the chemical environment of fragments atoms selected for pairing, the distance between connected atom pairs and proper organic chemistry rules are used to either directly connect two fragments by a single bond, or to link them by an appropriate linker derived from already fragmented ligand. The approach will enumerate drug-like and synthetically feasible compounds directly in the 3D target space.

SYBYL x2.1.1, Certara USA Inc., Princeton, U.S.A.

Szybki, Filter: OpenEye Scientific Sofware, Santa Fe, U.S.A.

Corina: Molecular Networks GmbH, Nürnberg, Germany

IChem, DeLinker, rdkit, POEM, PLANTS

Eguida, M., Schmitt, C., Hibert, M., Villa, P. and Rognan, D. (2022). Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J. Med. Chem., 65, 13771-13783

Da Silva, F., Desaphy, J. and Rognan, D. (2018) IChem: A Versatile Toolkit for Detecting, Comparing and Predicting Protein-Ligand Interactions, ChemMedChem, 13, 507-510

Desaphy, J. and Rognan, D. (2014) scPDBFrag: a database of protein-ligand interaction patterns for bioisosteric replacements. J.Chem. Inf. Model., 54, 1908-1918