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PubChem synonym filtering process using crowdsourcing.

Sunghwan Kim1, Bo Yu1, Qingliang Li1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.

Journal of Cheminformatics
|June 16, 2024
PubMed
Summary
This summary is machine-generated.

PubChem implemented a crowdsourcing strategy to improve chemical name-structure associations. Strategy I, using a 60% threshold, best resolved discrepancies for accurate chemical searching and linking.

Keywords:
Chemical databaseChemical name-structure associationCrowdsourcingCrowdvotingDatabase searchMedical Subject Headings (MeSH)PubChem

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Area of Science:

  • Chemical Informatics
  • Bioinformatics
  • Data Science

Background:

  • PubChem is a major chemical database with over 100 million structures.
  • Searching chemicals by name (synonym) is a frequent user task.
  • Existing synonym-structure associations have significant discrepancies, hindering accurate retrieval.

Purpose of the Study:

  • To develop and evaluate a crowdsourcing strategy for filtering chemical synonym-structure associations in PubChem.
  • To resolve discrepancies in both synonym-structure and chemical-MeSH associations.
  • To improve the reliability of chemical name searches and links to literature.

Main Methods:

  • Analyzed four crowd-voting strategies varying in consistency thresholds (60% vs. 70%) and depositor vote resolution.
  • Evaluated agreement at six levels of chemical equivalency (isotopic, stereochemical, connectivity).
  • Implemented the most effective strategy (Strategy I) in PubChem's data cleaning pipeline.

Main Results:

  • Strategy I (one vote per depositor, 60% threshold) yielded the most synonyms per structure.
  • This strategy effectively disambiguated the highest number of synonym-structure associations across equivalency levels.
  • All four strategies showed comparable performance, indicating the viability of crowdsourcing.

Conclusions:

  • A crowdsourcing-based filtering process significantly enhances the quality of chemical name-structure associations in PubChem.
  • Strategy I provides a robust method for resolving data discrepancies and improving data consistency.
  • This approach serves as a crucial step for quality control in large-scale chemical databases.