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

Using a genetic algorithm to identify common structural features in sets of ligands

J D Holliday1, P Willett

  • 1Krebs Institute for Biomolecular Research, University of Sheffield, UK.

Journal of Molecular Graphics & Modelling
|August 1, 1997
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

Quickest Detection of COVID-19 Pandemic Onset.

IEEE signal processing letters·2021
Same author

A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic.

Statistics in medicine·2013
Same author

Performance of an optimum receiver designed for pattern recognition with nonoverlapping target and scene noise.

Applied optics·2010
Same author

Approximate performance of the nonlinear joint transform correlator in signallike noise.

Applied optics·2010
Same author

Optimum receiver design for pattern recognition with nonoverlapping target and scene noise.

Optics letters·2009
Same author

Analysis of image detection based on fourier plane nonlinear filtering in a joint transform correlator.

Applied optics·2008
Same journal

Artificial intelligence-assisted QSAR modeling of phenolic monoterpenes targeting Helicobacter pylori.

Journal of molecular graphics & modelling·2026
Same journal

Covalent character of Cellobiose-Water hydrogen bonds revealed by ELF and QTAIM for enhanced dewatering and reactivity.

Journal of molecular graphics & modelling·2026
Same journal

Residue-level insights into SGLT2 inhibition and Nav1.5 selectivity of gliflozin derivatives: A molecular dynamics and pharmacophore-guided study.

Journal of molecular graphics & modelling·2026
Same journal

A benchmarking-informed structure-based virtual screening strategy targeting Lm-PTR1: Leveraging the Northern African natural products database.

Journal of molecular graphics & modelling·2026
Same journal

In Silico identification of natural and synthetic inhibitors targeting KRAS mutants (G12D, G12V, and G12C) and wild-type in pancreatic cancer.

Journal of molecular graphics & modelling·2026
Same journal

Structural evolution, mechanical and thermal stability of 7-40 mol% yttria-stabilized zirconia: First-principles investigation.

Journal of molecular graphics & modelling·2026
See all related articles

This study introduces MPHIL, a novel pharmacophore mapping program. MPHIL identifies common 3D patterns in molecules with biological activity, offering flexibility beyond existing methods.

Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Drug Discovery

Background:

  • Pharmacophore mapping is crucial for understanding molecular interactions and designing new drugs.
  • Existing methods often require strict adherence to identical pharmacophore patterns across all molecules.
  • This limitation can hinder the identification of conserved features in diverse compound sets.

Purpose of the Study:

  • To develop a flexible pharmacophore mapping program, MPHIL (Mapping Pharmacophores in Ligands).
  • To identify the smallest common 3D pharmacophore pattern shared by a set of biologically active molecules.
  • To overcome limitations of existing methods by allowing for variations in shared patterns.

Main Methods:

  • Utilizes a two-stage genetic algorithm (GA) approach.

Related Experiment Videos

  • An initial GA rapidly suggests potential pharmacophore point combinations.
  • A second GA refines these suggestions to identify the final 3D pharmacophore pattern.

    • Main Results:

    • MPHIL successfully identifies common 3D pharmacophore patterns within sets of molecules.
    • The program is designed to find patterns with at least 'm' common points, accommodating user-defined parameters.
    • It offers a more flexible approach compared to methods requiring exact pattern matching.

    Conclusions:

    • MPHIL provides a versatile tool for pharmacophore mapping in drug discovery.
    • Its flexible approach enhances the ability to identify conserved pharmacophoric features in diverse molecular datasets.
    • The genetic algorithm-based strategy enables efficient identification of relevant 3D patterns.