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Database fingerprint (DFP): an approach to represent molecular databases.

Eli Fernández-de Gortari1, César R García-Jacas2,3, Karina Martinez-Mayorga2

  • 1Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico City, Mexico.

Journal of Cheminformatics
|February 23, 2017
PubMed
Summary

A novel database fingerprint (DFP) represents entire compound libraries as a single binary fingerprint, reducing storage and computational needs. This method aids in assessing molecular library diversity and inter-library relationships for chemoinformatics applications.

Keywords:
DiversityInformation contentMolecular fingerprintsShannon entropySimilarity

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Area of Science:

  • Chemoinformatics
  • Computational Chemistry
  • Data Science

Background:

  • Molecular fingerprints are crucial for chemoinformatics tasks like diversity analysis and similarity searching.
  • Analyzing large chemical libraries with traditional fingerprints leads to significant storage and computational challenges due to data redundancy.
  • Existing methods often result in information loss or inefficiency when representing extensive compound collections.

Purpose of the Study:

  • To develop a novel approach for representing entire compound libraries using a single binary fingerprint.
  • To create a Database Fingerprint (DFP) that efficiently captures essential information from molecular databases.
  • To enable effective comparison and diversity assessment of molecular libraries.

Main Methods:

  • Development of the Database Fingerprint (DFP) concept.
  • Illustration using MACCS keys fingerprints on 10 diverse chemical datasets.
  • Application with PubChem fingerprints on datasets up to 25,000 molecules.
  • Performance evaluation using differential Shannon entropy, k-mean clustering, and DFP/Tanimoto similarity.

Main Results:

  • A general method to represent a whole compound library with a single binary fingerprint was successfully developed.
  • The DFP effectively captures key information for characterizing chemical space and assessing library diversity.
  • The DFP demonstrated potential in comparing and analyzing inter-library relationships.

Conclusions:

  • The Database Fingerprint (DFP) offers a powerful tool for efficient molecular library analysis and comparison.
  • DFP facilitates chemical space characterization and has potential applications in virtual screening.
  • Future work includes applying DFP to virtual screening and developing DFP for various fingerprint types and datasets.