ShinyTPs: Curating Transformation Products from Text Mining Results

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Toxicology

Background:

• Transformation product (TP) data is crucial for assessing chemical hazards but is often limited and not FAIR (Findable, Accessible, Interoperable, Reusable).
• Improving data accessibility for TPs supports identification workflows and machine learning applications.

Purpose of the Study:

• To develop ShinyTPs, an R package for curating transformation information from text-mined PubChem data.
• To enhance the FAIRness of existing TP knowledge and facilitate TP identification.

Main Methods:

• ShinyTPs was developed to curate and visualize text-mined chemical reaction data from PubChem.
• The application was used to prioritize TP retrieval for 436 tentatively identified compounds.
• Curated reactions were added to the PubChem Transformations library for use with the patRoon workflow.

Main Results:

• ShinyTPs extracted 645 reactions (496 compounds), with 319 new to PubChem.
• The curated library aided in the tentative identification of 72 compounds.
• 18% of identified compounds were curated using ShinyTPs, demonstrating its utility.

Conclusions:

• ShinyTPs effectively curates and FAIRifies TP information, improving data accessibility.
• The application supports TP identification in non-target analysis, contributing to better risk assessment.
• FAIRifying TP data is a practical step towards robust chemical safety evaluations.