Cell2Chem

Cell2Chem maps organisms to their biosynthesized compounds and predicts biosynthetic reaction pathways and enzyme functions to enable systematic exploration of biosynthetic chemical spaces.

Key Features:

• Extensive Database: Contains records for 40,370 species and 125,212 compounds.
• Organism–Compound Linking: Associates organisms with their biosynthesized compounds and elucidates processes involved in compound production.
• Co-occurrence Networks and NLP: Employs co-occurrence networks alongside natural language processing (NLP) to identify and systematize organism–chemical associations.
• In Silico Prediction Methods: Uses in silico prediction methods to propose reaction pathways and predict catalytic enzyme functions.

Scientific Applications:

• Exploration of Biosynthetic Chemical Spaces: Enables systematic analysis of explored and unexplored biosynthetic chemical spaces.
• Identification of Synthesized Compounds: Supports identification of compounds synthesized by living cell strains.
• Discovery of Pathways and Enzyme Functions: Facilitates uncovering new biosynthetic pathways and enzyme functions.
• Support for Applied Research: Provides predictive insights applicable to drug discovery, metabolic engineering, and sustainable chemical production.

Methodology:

Integrates co-occurrence networks with natural language processing (NLP) to link organisms to chemical outputs and applies in silico prediction methods for reaction pathways and enzyme function prediction.