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

Structure-based maximal affinity model predicts small-molecule druggability.

Alan C Cheng1, Ryan G Coleman, Kathleen T Smyth

  • 1Department of Molecular Informatics, Research Technology Center, Pfizer Global Research & Development, Cambridge, Massachusetts 02139, USA. alan.cheng@amgen.com

Nature Biotechnology
|January 11, 2007
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

Cellular nucleic acid-binding protein (CNBP) dependent cytokine programming shapes host defense against <i>Plasmodium</i> infection.

bioRxiv : the preprint server for biology·2026
Same author

Peptide binding at the gasdermin D exosite reveals the structural basis for targeting the site.

Acta crystallographica. Section D, Structural biology·2026
Same author

<i>N</i>‑Alkyl and <i>N</i>‑Aryl Aminopyrazole Spirocarbamates: A Two-Pronged Lead Optimization Strategy to Identify Orally Bioavailable Plasma Kallikrein Inhibitors.

ACS medicinal chemistry letters·2026
Same author

Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery.

Journal of chemical information and modeling·2025
Same author

Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models.

Journal of chemical information and modeling·2025
Same author

Design and Optimization of Selectivity-Tunable Toll-like Receptor 7/8 Agonists as Novel Antibody-Drug Conjugate Payloads.

Journal of medicinal chemistry·2024
Same journal

A genome-scale CRISPRi perturbation atlas of human induced pluripotent stem cells.

Nature biotechnology·2026
Same journal

Prime editing for precise genome engineering and modulation of fungal metabolism.

Nature biotechnology·2026
Same journal

Retargeted serine integrases for one-step, precise integration of large DNA sequences in human cells.

Nature biotechnology·2026
Same journal

A retargeted recombinase for precise insertion of large DNA.

Nature biotechnology·2026
Same journal

Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles.

Nature biotechnology·2026
Same journal

Spatially resolved profiling of extracellular vesicles in tissues with Spatial-EV-seq.

Nature biotechnology·2026
See all related articles

Predicting drug target druggability early in small-molecule drug discovery is crucial. A biophysical model using crystal structures accurately estimates maximal drug affinity, guiding development and reducing project failures.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Lead generation is a significant challenge in small-molecule drug discovery.
  • Approximately 60% of drug discovery projects fail due to insufficient lead compounds or difficulties in optimizing them for drug-like properties.
  • Identifying less-druggable targets early can save substantial resources.

Purpose of the Study:

  • To develop and validate a model-based approach for predicting target druggability using only the target binding site's crystal structure.
  • To quantitatively estimate the maximal achievable affinity for drug-like molecules against specific targets.
  • To correlate predicted druggability with actual drug discovery outcomes.

Main Methods:

  • Utilizing basic biophysical principles to construct a predictive model.

Related Experiment Videos

  • Analyzing crystal structures of target binding sites.
  • Quantitatively estimating maximal molecular affinity.
  • Experimental validation through high-throughput screening of compound libraries.

    • Main Results:

    • The model-based approach successfully predicted target druggability based solely on crystal structure data.
    • Calculated maximal affinity values showed a positive correlation with observed drug discovery success.
    • Experimental screening confirmed predictions for two test cases, demonstrating the model's utility.

    Conclusions:

    • A model using biophysical principles and crystal structures can effectively predict target druggability.
    • This approach aids in identifying challenging targets early in the drug discovery pipeline.
    • The findings offer strategies for overcoming difficulties associated with less-druggable targets.