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 Experiment Videos

Predicting protein druggability.

Philip J Hajduk1, Jeffrey R Huth, Christin Tse

  • 1Pharmaceutical Discovery Division GPRD, Abbott Laboratories, R46Y, AP-10, 100 Abbott Park Road, Abbott Park, IL 60064-3500 USA. philip.hajduk@abbott.com

Drug Discovery Today
|December 27, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Workers' compensation costs for healthcare caregivers: Home healthcare, long-term care, and hospital nurses and nursing aides.

American journal of industrial medicine·2021
Same author

Development of Orally Efficacious Allosteric Inhibitors of TNFα via Fragment-Based Drug Design.

Journal of medicinal chemistry·2020
Same author

Labeled Ligand Displacement: Extending NMR-Based Screening of Protein Targets.

ACS medicinal chemistry letters·2014
Same author

Development of a high-content screening assay panel to accelerate mechanism of action studies for oncology research.

Journal of biomolecular screening·2012
Same author

Fragment-based lead discovery: challenges and opportunities.

Journal of computer-aided molecular design·2011
Same author

Identification and characterization of mGlu3 ligands using a high throughput FLIPR assay for detection of agonists, antagonists, and allosteric modulators.

Combinatorial chemistry & high throughput screening·2011
Same journal

From biologics to small-molecule modulators: The evolving landscape of interleukin-targeted therapeutics.

Drug discovery today·2026
Same journal

Targeting the GLP-1 receptor pathways for dual management of obesity and depression.

Drug discovery today·2026
Same journal

Chemical intervention strategies targeting MYC for cancer therapy.

Drug discovery today·2026
Same journal

How many protein pairs can we chemically target?

Drug discovery today·2026
Same journal

From trial-and-error to inverse design: how AI is redefining drug delivery systems.

Drug discovery today·2026
Same journal

Critical evaluation of the key mediators causing life-threatening symptoms during human anaphylaxis.

Drug discovery today·2026
See all related articles

Predicting protein druggability from 3D structures aids drug discovery. This approach leverages structural genomics data to identify promising drug targets for validation and selection.

Area of Science:

  • Structural biology and computational chemistry
  • Drug discovery and development

Background:

  • Predicting protein-small molecule interactions from 3D structure is a key challenge.
  • Structural genomics initiatives have generated vast amounts of protein structure data.
  • Identifying druggable protein targets is crucial for efficient drug development.

Purpose of the Study:

  • To discuss the utility of tools for characterizing protein targets.
  • To describe strategies for integrating protein druggability data into target selection.
  • To highlight the potential of 3D structure-based druggability prediction in drug discovery.

Main Methods:

  • Characterization of protein targets using computational tools.
  • Integration of protein druggability data with bioinformatic approaches.

Related Experiment Videos

  • Analysis of 3D protein structures for binding site prediction.

    • Main Results:

    • Discussion of the potential utility of protein target characterization tools.
    • Description of strategies for optimal integration of druggability data.
    • Highlighting the value of druggability predictions for target identification and validation.

    Conclusions:

    • Accurate prediction of protein druggability can significantly enhance drug discovery pipelines.
    • Leveraging structural genomics data through druggability assessment is a promising strategy.
    • Integrating druggability data with bioinformatics optimizes target selection for drug development.