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

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.9K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
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...
4.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026
Same author

Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.

Journal of cheminformatics·2026
Same author

Learning the PTM code through a coarse-to-fine mechanism-aware framework.

Nature communications·2026
Same author

A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention.

Nature communications·2026
Same author

LamNet: an alchemical-path-aware graph neural network to accelerate binding free energy calculations for drug discovery and beyond.

National science review·2026
Same author

Management of essential thrombocythaemia-associated toe ulcer with a novel topical macrophage-modulating cream containing extract of <i>Plectranthus amboinicus</i>.

Journal of wound care·2026
Same journal

Targeting developmental reprogramming: hPSC insights for cancer interception.

Trends in pharmacological sciences·2026
Same journal

July 2026 issue first authors.

Trends in pharmacological sciences·2026
Same journal

Chronobiomaterials for circadian-aligned brain therapeutics.

Trends in pharmacological sciences·2026
Same journal

Biosensors for translatable GPCR bias.

Trends in pharmacological sciences·2026
Same journal

ECM stiffness and epigenetics in organ fibrosis.

Trends in pharmacological sciences·2026
Same journal

Which HTT transcript to lower?

Trends in pharmacological sciences·2026
See all related articles

Related Experiment Video

Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.6K

Harnessing deep learning for enhanced ligand docking.

Xujun Zhang1, Chao Shen1, Chang-Yu Hsieh2

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China.

Trends in Pharmacological Sciences
|December 30, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) can improve ligand docking (LD) accuracy and speed for protein-ligand binding predictions. This technology offers a promising approach to enhance virtual screening (VS) in drug discovery.

Keywords:
deep learningligand dockingscoring functionvirtual screening

More Related Videos

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

11.3K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.4K

Related Experiment Videos

Last Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.6K
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

11.3K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.4K

Area of Science:

  • Computational chemistry
  • Structural biology
  • Bioinformatics

Background:

  • Ligand docking (LD) is crucial for predicting protein-ligand (PL) binding.
  • Current LD methods face limitations in accuracy and speed.
  • Virtual screening (VS) relies heavily on efficient LD techniques.

Purpose of the Study:

  • To explore the potential of deep learning (DL) in addressing LD challenges.
  • To review recent advancements in DL for LD.
  • To project future trends of DL in computational drug discovery.

Main Methods:

  • Review of existing literature on deep learning applications in ligand docking.
  • Analysis of recent advancements and methodologies.
  • Discussion of future research directions and potential impact.

Main Results:

  • Deep learning models show promise in improving LD accuracy.
  • DL techniques can potentially accelerate LD processes.
  • Integration of DL is expected to enhance virtual screening efficiency.

Conclusions:

  • Deep learning offers a transformative approach to enhance ligand docking.
  • DL advancements are key to overcoming current limitations in LD speed and accuracy.
  • The future of virtual screening will likely involve significant integration of deep learning.