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

New developments in PEST shape/property hybrid descriptors.

Curt M Breneman1, C Matthew Sundling, N Sukumar

  • 1Department of Chemistry, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.

Journal of Computer-Aided Molecular Design
|September 19, 2003
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

Peripheral and central vestibular neuromodulation improve postural control in adolescent idiopathic scoliosis: a randomized, sham-controlled, multi-arm intervention study.

Journal of neuroengineering and rehabilitation·2026
Same author

Spectral fluctuations and crossovers in multilayer network.

Physical review. E·2026
Same author

ChemScreener: an active learning enabled hit discovery workflow with WDR5 inhibitor case study.

Journal of cheminformatics·2026
Same author

Integrated network pharmacology and metabolomics to analyze the mechanism of Shaofu Zhuyu Decoction in treatment of dysmenorrhea of cold coagulation and blood stasis.

Chinese herbal medicines·2026
Same author

Shared-autosampler parallel LC-MS/MS platform for high-throughput multi-analyte quantification in complex matrices.

Journal of chromatography. A·2026
Same author

An exploratory study on the preparation of metallic rhenium (Re) and ReO3 via non-contact solution plasma electrolysis.

PloS one·2025

Hybrid descriptors combining shape, property, and topological information enhance quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models. Property-Encoded Surface Translator (PEST) and RECON algorithms offer efficient computation of these advanced chemical descriptors.

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Quantitative structure-activity relationships (QSAR)

Background:

  • Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models benefit from advanced molecular descriptors.
  • Hybrid descriptors integrating diverse chemical information show increased predictive power.
  • Novel computational methods are needed for efficient descriptor generation.

Purpose of the Study:

  • To introduce and evaluate hybrid shape/property descriptors for enhanced QSAR and QSPR modeling.
  • To present the Property-Encoded Surface Translator (PEST) and RECON algorithms for descriptor computation.
  • To explore the application of these descriptors in virtual high-throughput screening.

Main Methods:

  • Computation of Property-Encoded Surface Translator (PEST) descriptors using electron density surfaces and electronic properties.

Related Experiment Videos

  • Utilizing atomic fragment-based TAE/RECON property-encoded surface reconstructions.
  • Implementing rapid fragment-based wavelet coefficient descriptor (WCD) computation within RECON and PEST algorithms.
  • Applying TAE properties for molecular surface autocorrelation analysis.

    • Main Results:

    • Hybrid descriptors significantly improve the predictive capabilities of QSAR and QSPR models.
    • PEST and RECON algorithms provide a compact encoding of complex chemical information.
    • Wavelet coefficient descriptors (WCD) are efficiently computed using these algorithms.
    • The methodology is suitable for virtual high-throughput analysis.

    Conclusions:

    • The integration of hybrid descriptors, computed via PEST and RECON, advances predictive modeling in cheminformatics.
    • These methods offer efficient and compact representation of molecular properties.
    • The RECON/PEST methodology shows promise for virtual screening and molecular analysis.