Covalent Inhibitors are of interest for therapeutic intervention in the treatment of a number of human diseases, including COVID-19, where the main coronavirus protease (SARS-CoV-2 Mpro) is an important target for drug development. Enzymatic Covalent Inhibitors affords a unique set of advantages and it is a common strategy in cancer therapy. Covalent inhibitor can be also used for Leishmanioses and Chagas Disease. The development and application of computer-assisted tools to predict the biological activity of small covalent ligands is key for drug discovery. The complete drug discovery pipeline includes high-throughput screening of molecular libraries and the optimization of lead compounds. Encouragingly, a myriad of computational strategies has been delivered for developing and optimizing drug molecules that can be also applied for covalent inhibitors. The detailed understanding of the protein-ligand interactions is fundamental for discovery of novel drug that can treat important illness. Besides, sophisticated approaches are used for predicting the binding free energies of enzymatic covalent inhibitors, such as free energy perturbation. Quantum mechanics and molecular mechanics (QM/MM) approaches also have a potential application for designing enzymatic covalent inhibitors.

Molecular modeling may be used to predict the three-dimensional shapes of small covalent ligands and their targets and can be used to tune key molecular interaction in order to improve the drug action. The purpose of the present collection is to present contributions dedicated to Enzymatic Covalent Inhibitors. This Research Topic is dedicated to covering the development and application of multi-disciplinary approach for developing new covalent inhibitors and explore details of protein-ligand interactions. Manuscripts that combine experimental and computational results are also welcome. In summary, the aim of this collection is to motivate the publication of original research and review articles covering all aspect of Enzymatic Covalent Inhibitors.
We welcome submissions covering, but not limited to:

• The development of new approaches for Study Covalent Inhibitors
• The application of computational modeling for Covalent Inhibitors
• The prediction of binding free energy
• High-throughput screening of molecular libraries
• Molecular modeling of covalent inhibitors
• Covalent Docking

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Frontiers in Molecular Biosciences

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