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

A maximum common substructure-based algorithm for searching and predicting drug-like compounds.

Yiqun Cao1, Tao Jiang, Thomas Girke

  • 1Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ycao@cs.ucr.edu

Bioinformatics (Oxford, England)
|July 1, 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

Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency.

American journal of human genetics·2011
Same author

Uncover disease genes by maximizing information flow in the phenome-interactome network.

Bioinformatics (Oxford, England)·2011
Same author

Clinical implications of activated leukocyte cell adhesion molecule expression in breast cancer.

Molecular biology reports·2011
Same author

Estimation of allele frequency and association mapping using next-generation sequencing data.

BMC bioinformatics·2011
Same author

Learning curve of transumbilical single incision laparoscopic cholecystectomy (SILS): a preliminary study of 80 selected patients with benign gallbladder diseases.

World journal of surgery·2011
Same author

Highly plasmon-enhanced upconversion emissions from Au@β-NaYF4:Yb,Tm hybrid nanostructures.

Chemical communications (Cambridge, England)·2011
Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026
Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026
Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026
Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026
Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026
Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026
See all related articles

A new algorithm efficiently identifies local structural similarities in compounds. Combining this with other measures improves prediction of biological activity, aiding drug discovery.

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Predicting biologically active compounds is crucial for high-throughput screening (HTS) in drug discovery.
  • Traditional similarity measures often focus on global structures and can be rigid.
  • Maximum common substructure (MCS) offers a flexible alternative for predicting bioactive compounds.

Purpose of the Study:

  • To introduce a novel backtracking algorithm for Maximum Common Substructure (MCS) identification.
  • To propose a 'basis compounds' concept for efficient prediction and clustering of bioactive compounds.
  • To evaluate the performance of MCS-based similarity measures combined with machine learning.

Main Methods:

  • Developed a new backtracking algorithm for MCS calculation, emphasizing flexibility and local similarity identification.

Related Experiment Videos

  • Introduced the 'basis compounds' concept for vectorization, enabling integration of MCS and traditional similarity measures.
  • Utilized Support Vector Machines (SVMs) to assess the performance of MCS-based similarity and basis compound vectorization on empirical datasets.

    • Main Results:

    • The proposed MCS algorithm efficiently identifies local structural similarities, outperforming rigid global measures.
    • The basis compound vectorization method facilitates the combination of MCS-based and traditional similarity measures.
    • SVM models integrating MCS and atom pair descriptors demonstrated enhanced specificity and sensitivity in predicting biological activities.

    Conclusions:

    • The novel MCS algorithm provides a flexible and efficient approach for identifying local structural similarities.
    • The basis compound concept effectively integrates diverse similarity measures for improved compound analysis.
    • Combining MCS-based similarity with other descriptors significantly enhances the accuracy of predicting compound biological activities.