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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

764
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...
764
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.0K
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...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions.

Scientific reports·2026
Same author

Fragment-Guided New Therapeutic Molecule Discovery and Mapping of Clinically Relevant Interactomes.

Journal of chemical information and modeling·2026
Same author

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel.

Scientific reports·2024
Same author

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals.

Sensors (Basel, Switzerland)·2023
Same author

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients.

Scientific reports·2023
Same author

Accelerating COVID-19 Drug Discovery with High-Performance Computing.

Methods in molecular biology (Clifton, N.J.)·2023

Related Experiment Video

Updated: Jul 16, 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

318

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations,

Reuben L Martin1,2, Alexander Heifetz3, Mike J Bodkin4

  • 1Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK. reuben.martin.18@ucl.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|September 13, 2023
PubMed
Summary

Structure-based drug design (SBDD) accelerates drug discovery by computationally screening potential drug candidates. High-throughput SBDD enhances traditional methods, improving success rates in identifying effective therapeutics.

Keywords:
Drug developmentFMOFragment molecular orbitalsHigh-performance computingLigand dockingMolecular dynamicsStructure-based drug designVirtual screening

More Related Videos

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

1.2K
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

71

Related Experiment Videos

Last Updated: Jul 16, 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

318
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

1.2K
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

71

Area of Science:

  • Computational chemistry and pharmacology
  • Drug discovery and development

Background:

  • Traditional high-throughput screening (HTS) for drug discovery has a low success rate (~1%).
  • Structure-based drug design (SBDD) offers a computational alternative to accelerate lead identification.

Approach:

  • Utilizes target protein structural data and large compound libraries for virtual screening.
  • Employs computational techniques like high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics.
  • Compares the advantages and limitations of SBDD against traditional drug discovery methods.

Key Points:

  • High-throughput SBDD (HT-SBDD) aims to significantly improve the success rate of HTS.
  • Focuses on the theory and application of advanced computational techniques in drug design.
  • Highlights the critical role of high-performance computing (HPC) in enabling computationally intensive SBDD.

Conclusions:

  • SBDD is an evolving, cost-effective tool for faster lead drug discovery.
  • HT-SBDD has the potential to revolutionize virtual screening and enhance therapeutic development.
  • High-performance computing is essential for the future advancement of computational drug discovery.