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Tautomerism in large databases.

Markus Sitzmann1, Wolf-Dietrich Ihlenfeldt, Marc C Nicklaus

  • 1Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles St., Frederick, MD 21702, USA.

Journal of Computer-Aided Molecular Design
|June 1, 2010
PubMed
Summary
This summary is machine-generated.

Tautomerism analysis of over 100 million real chemical compounds revealed that over two-thirds exhibit tautomerism. This widespread phenomenon, impacting nearly 10% of records across databases, highlights the importance of considering tautomeric forms in chemical structure analysis.

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Area of Science:

  • Computational chemistry
  • Chemoinformatics
  • Medicinal chemistry

Background:

  • The Chemical Structure DataBase (CSDB) aggregates over 150 small-molecule databases, containing 103.5 million real chemical structures.
  • Understanding tautomerism is crucial for accurate chemical structure representation and analysis.

Purpose of the Study:

  • To perform tautomerism analysis on a large dataset of real chemical compounds.
  • To determine the prevalence of tautomerism and tautomeric overlap within and across databases.

Main Methods:

  • Utilized the NCI CADD Group's Chemical Structure DataBase (CSDB).
  • Employed calculable chemical structure identifiers and a novel scoring scheme for canonical tautomer definition.
  • Applied CACTVS chemoinformatics toolkit with 21 SMIRKS-defined tautomerism transform rules.

Main Results:

  • Tautomerism was possible for over two-thirds of unique structures in the CSDB.
  • Calculated 680 million tautomers from the original structure records.
  • Observed tautomeric overlap within individual databases (0.3%–2%) and across all databases (nearly 10%).

Conclusions:

  • Tautomerism is a prevalent characteristic of real chemical compounds.
  • Significant tautomeric overlap exists within and across chemical databases.
  • These findings underscore the need to account for tautomerism in chemical data management and analysis.