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

12.8K
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...
12.8K
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

12.8K
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:
12.8K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

684
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
684
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
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.7K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Integrating multimodal features with deep learning for protein solubility prediction.

Journal of cheminformatics·2026
Same author

Versatile Synthesis of Cyclopentenones via Skeletal Editing of Phenols.

Journal of the American Chemical Society·2026
Same author

Discussions on the generalization of HybridSP on more equivalent benchmarks.

Briefings in bioinformatics·2026
Same author

Revealing the intricate mechanism governing the pH-dependent activity of a quintessential representative of flavoproteins, glucose oxidase.

Fundamental research·2026
Same author

Morphology-guided deep learning for nanoparticle agglomeration diagnostic assays.

Scientific reports·2026
Same author

Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations.

Journal of chemical information and modeling·2026

Related Experiment Video

Updated: Jun 15, 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

2.4K

Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention.

Yueming Yin, Hilbert Yuen In Lam, Yuguang Mu

    IEEE Journal of Biomedical and Health Informatics
    |August 23, 2024
    PubMed
    Summary

    This study introduces a novel deep learning model, Drug-Target Interaction Graph Neural Network (DTIGN), to improve bioactivity prediction by integrating drug-target interactions. DTIGN significantly enhances prediction accuracy for drug candidates.

    More Related Videos

    Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
    08:15

    Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease

    Published on: May 10, 2024

    524
    Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
    10:29

    Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

    Published on: May 9, 2025

    476

    Related Experiment Videos

    Last Updated: Jun 15, 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

    2.4K
    Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
    08:15

    Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease

    Published on: May 10, 2024

    524
    Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
    10:29

    Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

    Published on: May 9, 2025

    476

    Area of Science:

    • Computational chemistry
    • Pharmacology
    • Bioinformatics

    Background:

    • Bioactivity prediction is crucial for drug discovery, traditionally relying on Quantitative Structure-Activity Relationship (QSAR) models.
    • Existing deep learning methods primarily focus on ligand structure, neglecting other critical factors like drug-target interactions.
    • Bioactivity is influenced by a complex interplay of ligand structure, drug-target binding, signaling pathways, and biological context.

    Purpose of the Study:

    • To develop an advanced deep learning model that integrates drug-target interactions for more accurate bioactivity prediction.
    • To enhance early-stage drug candidate screening by improving the identification of potentially active molecules.
    • To establish a new benchmark dataset for evaluating bioactivity prediction models in the context of protein-ligand complexes.

    Main Methods:

    • Devised a Drug-Target Interaction Graph Neural Network (DTIGN) model, incorporating interatomic forces into intermolecular graphs.
    • Utilized multi-head self-attention within DTIGN to identify optimal binding pockets and poses from molecular docking data.
    • Employed semi-supervised learning with limited native structures from crystal databases to refine bioactivity predictions.

    Main Results:

    • The DTIGN model demonstrated superior performance in bioactivity prediction compared to existing methods.
    • Achieved an average performance improvement of 27.03% over 9 leading deep learning-based bioactivity prediction techniques.
    • Established a novel benchmark dataset facilitating the evaluation of bioactivity prediction models for protein-ligand complexes.

    Conclusions:

    • Integrating drug-target interactions significantly improves the accuracy of bioactivity prediction models.
    • The DTIGN model offers a powerful new approach for efficient and accurate drug candidate screening.
    • The developed benchmark dataset will advance research in computational drug discovery and personalized medicine.