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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...

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Related Experiment Video

Updated: May 30, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

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Published on: September 2, 2020

How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint

Kathrin Heikamp1, Jürgen Bajorath

  • 1Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.

Journal of Chemical Information and Modeling
|July 29, 2011
PubMed
Summary

Two-dimensional (2D) fingerprints enable scaffold hopping in virtual screening by identifying diverse molecules. Feature selection methods like gain ratio enhance this ability, revealing how distinct molecular subsets are detected.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Two-dimensional (2D) fingerprints demonstrate scaffold hopping in virtual screening.
  • These descriptors primarily focus on structural and topological similarity.
  • The mechanism behind fingerprint-driven enrichment of diverse molecules is poorly understood.

Purpose of the Study:

  • To investigate the mechanism of scaffold hopping using atom environment fingerprints.
  • To explore the effectiveness of feature selection methods in enhancing fingerprint utility.
  • To analyze how fingerprints enrich structurally diverse compounds.

Main Methods:

  • Similarity search calculations were performed on 120 compound activity classes.
  • Atom environment fingerprints were utilized.
  • Feature selection methods, Kullback-Leibler divergence and gain ratio analysis, were applied to reduce fingerprints.
  • Compound recall characteristics of original and reduced fingerprints were analyzed.

Main Results:

  • Gain ratio analysis proved to be an effective fingerprint feature selection approach.
  • Small sets of fingerprint features successfully distinguished subsets of active compounds.
  • Compound recall often resulted from cumulative detection of distinct compound subsets by different features.

Conclusions:

  • The study provides a rationale for the scaffold hopping potential of 2D fingerprints.
  • Feature selection, particularly gain ratio, enhances the ability of fingerprints to identify diverse molecules.
  • Understanding these mechanisms can improve virtual screening strategies.