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

Scaffold hopping in drug discovery using inductive logic programming.

Kazuhisa Tsunoyama1, Ata Amini, Michael J E Sternberg

  • 1Computational Bioinformatics Laboratory, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, United Kingdom.

Journal of Chemical Information and Modeling
|May 7, 2008
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

Computational Resources for Molecular Biology 2026.

Journal of molecular biology·2026
Same author

3DSeqCheck: A Web-based Tool for Verifying Sequence Consistency Between a 3D Structure File and the Corresponding UniProt Entry.

Journal of molecular biology·2026
Same author

The aging of the AlphaFold database.

Nature structural & molecular biology·2025
Same author

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.

Machine learning·2025
Same author

Complete loss of SLC30A8 in humans improves glucose metabolism and beta cell function.

Diabetologia·2025
Same author

Computational Resources for Molecular Biology 2025.

Journal of molecular biology·2025
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
See all related articles

We introduce a new inductive logic programming (ILP) method for scaffold hopping in chemoinformatics. This ILP approach significantly outperforms existing methods in finding diverse, active compound structures and identifying key 3D features.

Area of Science:

  • Chemoinformatics
  • Computational chemistry
  • Drug discovery

Background:

  • Scaffold hopping is crucial for discovering novel drug candidates with desired biological activity.
  • Existing methods face limitations in identifying diverse and novel active scaffolds.
  • Unexpected side-effects or patent circumvention necessitate alternative compound structures.

Purpose of the Study:

  • To propose and evaluate a novel scaffold hopping method using inductive logic programming (ILP).
  • To compare the ILP-based method against established algorithms like CATS and CATS3D.
  • To demonstrate the ability of ILP to generate human-readable rules for scaffold hopping.

Main Methods:

  • Utilizing inductive logic programming (ILP) to learn rules from spatial relationships between pharmacophore types.

Related Experiment Videos

  • Applying the ILP method to 10 diverse datasets and comparing performance against CATS and CATS3D.
  • Assessing the retrieval of novel active scaffolds and the interpretability of learned rules.

    • Main Results:

    • The ILP-based scaffold hopping method significantly outperformed random selection.
    • The ILP method identified novel active scaffolds missed by CATS and CATS3D.
    • Learned rules provided insights into 3D features driving scaffold hopping, validated in a blind trial.

    Conclusions:

    • Inductive logic programming offers a valuable and effective approach for scaffold hopping in chemoinformatics.
    • The ILP method demonstrates superior performance and interpretability compared to existing algorithms.
    • ILP successfully generates rules that can guide the discovery of new active compounds.