Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Network Covalent Solids02:18

Network Covalent Solids

13.7K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.7K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

1.7K
1.7K
Van der Waals Interactions01:24

Van der Waals Interactions

65.2K
Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
65.2K
Covalent Bonds01:08

Covalent Bonds

8.1K
Overview
When two atoms share electrons to complete their valence shells, they create a covalent bond. An atom's electronegativity—the force with which shared electrons are pulled towards an atom—determines how the electrons are shared. Molecules formed with covalent bonds can be either polar or nonpolar. Atoms with similar electronegativities form nonpolar covalent bonds; the electrons are shared equally. Atoms with different electronegativities share electrons unequally,...
8.1K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.9K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

18.2K
18.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Toward a Random Background for Ligand Optimization.

bioRxiv : the preprint server for biology·2026
Same author

Predicting intracranial angioplasty failure using vessel wall MRI habitat radiomics and deep learning: a multicenter study.

Journal of neurointerventional surgery·2026
Same author

The therapeutic potential of epimedium and its bioactive flavonoids in hepatitis and cirrhosis: an integrative review.

Frontiers in cellular and infection microbiology·2026
Same author

Ultra-broadband single-stack mid-infrared semiconductor lasers grown by MOCVD.

Light, science & applications·2026
Same author

Sampling the Grand Canonical Ensemble with Multisite λ Dynamics.

The journal of physical chemistry letters·2026
Same author

Separation of Molecular Magnets via HPLC with EPR Online Monitoring.

Analytical chemistry·2026
Same journal

Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.

Journal of computer-aided molecular design·2026
Same journal

QUAD: a composite risk framework integrating uncertainty, applicability domain, and model disagreement for reliable QSAR predictions.

Journal of computer-aided molecular design·2026
Same journal

Comparative quantum-chemical investigation of 2-chloro-N-(4-methoxyphenyl)acetamide and 2-(4-methoxyphenylamino)-2-oxoethyl meth/acrylate: DFT, TD-DFT, and non-covalent interaction analyses.

Journal of computer-aided molecular design·2026
Same journal

Discovery of a novel CaMKⅡα inhibitor by machine learning, molecular docking and molecular dynamics simulation.

Journal of computer-aided molecular design·2026
Same journal

Explainable QSAR models of 5-HT1A receptor ligands using conceptual DFT descriptors and no-code machine learning tools.

Journal of computer-aided molecular design·2026
Same journal

Computational investigation of antioxidant activities and mechanisms of catechol analogues through a DFT study.

Journal of computer-aided molecular design·2026
See all related articles

Related Experiment Video

Updated: Aug 31, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

506

Covalent docking in CDOCKER.

Yujin Wu1, Charles L Brooks Iii2,3

  • 1Department of Chemistry, University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, Michigan, 48109, USA.

Journal of Computer-Aided Molecular Design
|August 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method for identifying targeted covalent inhibitors (TCIs), a crucial class of drugs. The developed covalent docking algorithm efficiently predicts lead compounds, accelerating drug discovery.

Keywords:
Covalent dockingRigid CDOCKERVirtual screening

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

313
Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
10:33

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

11.4K

Related Experiment Videos

Last Updated: Aug 31, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

506
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

313
Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
10:33

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

11.4K

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Medicinal chemistry

Background:

  • Targeted covalent inhibitors (TCIs) are vital in drug discovery, with ~30% of marketed drugs being TCIs, prized for potency and prolonged effects.
  • Despite toxicity concerns, TCIs offer therapeutic advantages like less frequent dosing and wider therapeutic margins.
  • Developing efficient computational methods for identifying novel covalent inhibitors is of significant interest.

Discussion:

  • This paper details the implementation and testing of a covalent docking methodology within Rigid CDOCKER.
  • A physics-based scoring function was optimized with a customizable covalent bond grid potential to model free energy changes during bond formation.
  • The covalent bond grid potential was fine-tuned for common covalent bond-forming reactions in TCIs.

Key Insights:

  • The new covalent docking method achieves an average runtime of 15 minutes per compound, comparable to or faster than existing algorithms.
  • Pose prediction accuracy is comparable to other established covalent docking methods, validated against a benchmark dataset.
  • A novel, large-scale benchmark dataset for covalent docking was created, featuring 8 receptor targets and diverse covalent bond chemistries.

Outlook:

  • The developed algorithm demonstrates significant potential in identifying lead compounds within vast chemical spaces.
  • The method achieved a high AUC of 0.909 for the CATK receptor target, highlighting its effectiveness.
  • Future work may involve further refinement and application of this method to discover new TCI drug candidates.