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

Druggability indices for protein targets derived from NMR-based screening data.

Philip J Hajduk1, Jeffrey R Huth, Stephen W Fesik

  • 1Global Pharmaceutical Research and Development, Abbott Laboratories, Abbott Park, Illinois 60064, USA. philip.hajduk@abbot.com

Journal of Medicinal Chemistry
|April 2, 2005
PubMed
Summary

This study reveals a predictive model for small molecule protein binding using binding site characteristics. The model accurately forecasts screening success rates and identifies druggable protein targets for drug discovery.

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

Identification of KLHL12 Ligands Using Fragment-Based Methods.

Journal of medicinal chemistry·2026
Same author

Discovery of Spiro[chromane-2,4'-piperidine] Derivatives as Irreversible Inhibitors of SARS-CoV-2 Papain-like Protease.

Journal of medicinal chemistry·2026
Same author

Correction to "Fragment-to-Lead Medicinal Chemistry Publications in 2024: A Tenth Annual Perspective".

Journal of medicinal chemistry·2026
Same author

Discovery of Fragment-Based Inhibitors of SARS-CoV-2 PL<sup>Pro</sup>.

Journal of medicinal chemistry·2026
Same author

Fragment-to-Lead Medicinal Chemistry Publications in 2024: A Tenth Annual Perspective.

Journal of medicinal chemistry·2025
Same author

Nuclear Magnetic Resonance-based fragment screen of the E3 ligase Fem-1 homolog B.

Protein science : a publication of the Protein Society·2025

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Understanding protein-ligand interactions is crucial for drug discovery.
  • Predicting binding affinity and druggability of protein targets remains a challenge.

Purpose of the Study:

  • To develop a predictive model for small molecule binding affinity to proteins.
  • To identify key protein binding site features that correlate with binding success.
  • To assess the druggability of protein targets using computational methods.

Main Methods:

  • Analysis of heteronuclear-NMR-based screening data.
  • Development of a quantitative structure-activity relationship (QSAR) model incorporating binding site parameters.
  • Validation of the model using independent datasets and prediction of protein target druggability.

Related Experiment Videos

Main Results:

  • A model combining polar/apolar surface area, surface complexity, and pocket dimensions accurately predicted experimental screening hit rates (R(2)=0.72, Q(2)=0.56).
  • The model correctly classified 94% of known druggable protein targets.
  • Identified key binding site characteristics influencing high-affinity binding.

Conclusions:

  • The developed model provides a robust method for predicting small molecule binding and assessing protein target druggability.
  • This approach facilitates target identification, virtual screening, and structure-based drug design.
  • Quantitative analysis of binding sites enhances target validation and drug development strategies.