AdditiveChem: A comprehensive bioinformatics knowledge-base for food additive chemicals

Area of Science:

• Food Science
• Toxicology
• Computational Chemistry

Background:

• Food additives are integral to the modern food industry, impacting production, processing, and storage.
• A comprehensive understanding of food additive properties, including molecular structure and toxicity, is crucial.
• Existing data on food additives is fragmented across multiple sources.

Purpose of the Study:

• To create a centralized database for food additives.
• To facilitate in silico preliminary evaluations of food additives.
• To enable exploration of structure-function relationships of food additives.

Main Methods:

• Curated data from 16 databases.
• Included molecular structure, properties, ADMET, biosynthesis, usage, and toxicity data.
• Developed an efficient search platform for AdditiveChem.

Main Results:

• AdditiveChem database contains >9064 types of food additives.
• Data includes molecular structure, chemical/physical properties, ADMET, biosynthesis, usage, toxicity, and human targets.
• Provides a platform for in silico preliminary evaluations.

Conclusions:

• AdditiveChem serves as a valuable resource for food additive research.
• The database supports preliminary safety and efficacy assessments.
• Facilitates deeper understanding of food additive behavior and impact.