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The ITS2 Database
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Statistical-based database fingerprint: chemical space dependent representation of compound databases.

Norberto Sánchez-Cruz1, José L Medina-Franco2

  • 1Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico. norberto.sc90@gmail.com.

Journal of Cheminformatics
|November 24, 2018
PubMed
Summary

A new statistical-based database fingerprint (SB-DFP) method simplifies compound database representation. This approach effectively captures common and distinct features for exploring target relationships and performing similarity searches in drug discovery.

Keywords:
Chemical spaceEpi-informaticsMolecular fingerprintsRepresentationSimilarity searching

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Area of Science:

  • Cheminformatics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Simplified compound database representations are crucial in cheminformatics.
  • Existing methods like modal fingerprints capture significant bits but lack generalizability.
  • A novel statistical-based database fingerprint (SB-DFP) is introduced for general compound database representation.

Purpose of the Study:

  • To introduce and validate a general method for creating single fingerprint representations of compound databases.
  • To demonstrate the utility of SB-DFP in exploring relationships between epigenetic targets and their associated compound sets.
  • To compare SB-DFP performance against other methods in similarity searching.

Main Methods:

  • Developed a statistical-based database fingerprint (SB-DFP) using binomial proportion comparisons.
  • Utilized a large, representative chemical space dataset (ZINC) as a reference.
  • Constructed SB-DFPs for 28 epigenetic target datasets using two different fingerprint designs.

Main Results:

  • SB-DFPs successfully captured both common and distinct features across different epigenetic target datasets.
  • In similarity searching tasks, SB-DFP performance was comparable or superior to existing methods for at least 20 out of 28 datasets.
  • The method proved effective in illustrating association relationships between epigenetic targets.

Conclusions:

  • SB-DFP offers a general and robust approach for representing compound datasets as single fingerprints.
  • The method is adaptable, allowing for the use of various fingerprint types and reference datasets.
  • SB-DFP is a valuable tool for analyzing target relationships and enhancing similarity searching in cheminformatics and drug discovery.