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Cambridge MedChem Consulting

Computational Prediction of covalent Inhibitors

Covalent Inhibitors are an increasingly important class of therapeutic agents.

A computational pipeline has been described by the London lab to predict suggest covalent analogs of non-covalent ligands DOI.

Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found 11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC50) values between 155 nM and 4.5 μM. Application against an existing SARS-CoV Mpro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 Mpro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.

RDKit was used for 2D molecular handling, conformation generation and RMSD calculation. RDKit: Open-source cheminformatics; version 2018.09.3; Marvin was used in the process of preparing the molecules for docking, Marvin 17.21.0, ChemAxon ( OpenBabel (http:// was used to switch between molecular file formats. DOCKovalent (London et al., 2014) was used for virtual covalent docking. The Covalentizer code is available at