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

1.2K
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...
1.2K
Drug Discovery: Overview01:26

Drug Discovery: Overview

9.4K
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...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Molecular Dynamics and Other HPC Simulations for Drug Discovery.

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

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach.

Journal of computer-aided molecular design·2020
Same author

Multitarget Approach for the Treatment of Alzheimer's Disease: Inhibition of Phosphodiesterase 9 (PDE9) and Histone Deacetylases (HDACs) Covering Diverse Selectivity Profiles.

ACS chemical neuroscience·2019
Same author

Discovery of in Vivo Chemical Probes for Treating Alzheimer's Disease: Dual Phosphodiesterase 5 (PDE5) and Class I Histone Deacetylase Selective Inhibitors.

ACS chemical neuroscience·2018
Same author

Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization.

Journal of cheminformatics·2018
Same author

Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen.

Journal of chemical information and modeling·2018

Related Experiment Video

Updated: Oct 12, 2025

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

6.0K

Fine tuning for success in structure-based virtual screening.

Emilie Pihan1, Martin Kotev2, Obdulia Rabal2

  • 1Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France. emilie.pihan@evotec.com.

Journal of Computer-Aided Molecular Design
|November 20, 2021
PubMed
Summary
This summary is machine-generated.

Structure-based virtual screening (SBVS) accelerates drug discovery by virtually screening millions of compounds. This study introduces an automated Knime workflow to optimize SBVS protocols, ensuring efficient and rational selection of the best computational methods for identifying potential drug candidates.

Keywords:
CalibrationDecoysDockingScoringStructure-based virtual 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

1.7K
Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin
06:29

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

5.7K

Related Experiment Videos

Last Updated: Oct 12, 2025

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

6.0K
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

1.7K
Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin
06:29

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

5.7K

Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Structural biology

Background:

  • Structure-based virtual screening (SBVS) is crucial for identifying potential drug candidates by computationally docking large compound libraries into protein targets.
  • The accuracy of SBVS is highly dependent on the chosen docking and scoring algorithms, necessitating a systematic approach for protocol selection.
  • Optimizing SBVS protocols is challenging due to the vast number of available algorithms and the variability of protein target sites.

Purpose of the Study:

  • To develop and present an automated calibration process for optimizing structure-based virtual screening protocols.
  • To reduce the time and effort required for selecting the most effective computational methods for drug discovery.
  • To demonstrate the application of the developed tool for setting up an optimal SBVS protocol against Retinoid X Receptor alpha.

Main Methods:

  • Implementation of an automated calibration process within a Knime workflow.
  • Creation of dedicated protein and compound test sets, including target structures, models, ligands, and decoys.
  • Automatic testing of 24 distinct scoring/rescoring protocols across various target conformations and graphical visualization of performance metrics.

Main Results:

  • The automated workflow significantly reduces the calibration phase duration through high-performance computing.
  • Systematic evaluation of multiple algorithms and target conformations enables a rational and optimal protocol selection.
  • Successful application demonstrated for optimizing SBVS against Retinoid X Receptor alpha.

Conclusions:

  • The developed automated calibration tool streamlines the optimization of SBVS protocols, enhancing efficiency in drug discovery.
  • This approach provides a rational basis for selecting the best computational strategy tailored to specific protein targets.
  • The workflow facilitates the identification of high-quality potential ligands for experimental validation, accelerating the drug development pipeline.