Headed to ACS San Diego? Join us for Happy Hour!
← Back to glossary

Covalent Docking

Method
Method
Method

Covalent Docking is a computational technique used to predict the binding mode of covalent ligands to their target proteins. Unlike traditional docking methods that only model non-covalent interactions, covalent docking also simulates the formation of a covalent bond between a reactive species in a ligand and a reactive species in a residue on the protein. This is particularly important for molecules designed to probe irreversible or reversible covalent binding mechanisms that can lead to enhanced potency and selectivity.

Importance in Computational Drug Discovery

  1. Enhanced Potency: Covalent inhibitors often exhibit higher potency due to them inherently having much slower off-rates or increased residence times.
  2. Improved Selectivity: By targeting specific residues that can form covalent bonds, these ligands can achieve greater selectivity, reducing off-target effects.
  3. Overcoming Resistance: Covalent inhibitors can be effective against targets with mutations that confer resistance to non-covalent inhibitors.
  4. Longer Duration of Action: The irreversible binding can result in a prolonged duration of action, which can be beneficial in some therapeutic settings.

Key Tools

1. Schrödinger's CovDock: A specialized tool for covalent docking that models the formation of covalent bonds between ligands and protein residues.

2. GROMACS: While primarily used for molecular dynamics simulations, it can be integrated with covalent docking studies to simulate the behavior of covalently bound complexes.

3. MOE (Molecular Operating Environment): Includes tools for covalent docking and can be used to design and optimize covalent inhibitors.

4. AutoDock: An open-source docking tool that can be adapted for covalent docking studies with appropriate modifications.

Literature

"An approach combining deep learning and molecule docking for drug discovery of cathepsin L"

  • Publication Date: 2023-02-27
  • DOI: 10.1080/17460441.2023.2174522
  • Summary: This study combines deep learning and the Schrödinger CovDock algorithm to identify potential cathepsin L inhibitors from a large database. The approach efficiently identifies molecules with better docking results than known inhibitors, demonstrating the potential of CovDock in large-scale drug discovery efforts.

"Comparative Evaluation of Covalent Docking Tools"

  • Publication Date: 2018-06-11
  • DOI: 10.1021/acs.jcim.8b00228
  • Summary: This paper evaluates six covalent docking tools, including CovDock, for their ability to reproduce experimental binding modes. The study highlights the strengths and weaknesses of each tool, providing guidelines for selecting the optimal docking tool based on warhead chemistry, ligand size, and protein accessibility.

"Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics"

  • Publication Date: 2016-08-17
  • DOI: 10.1021/ACS.JMEDCHEM.6B00399
  • Summary: Discusses the evolution of cosolvent-based molecular dynamics techniques, including their integration with covalent docking, to provide realistic assessments of binding hotspots and guide the design of covalent inhibitors.