Canopy: Heterograph AI Model for Metabolic Engineering Design.

Designing microbial strains to produce high-value chemicals efficiently is a core challenge in metabolic engineering. Current computational methods either rely on rigid constraint-based models or use tabular machine learning that discards the rich relational structure of biological data. This research introduces Canopy, a groundbreaking heterogeneous graph foundation model that addresses these limitations. Canopy integrates ten public and proprietary biological data sources, including genes, proteins, metabolites, and experimental results, into a massive unified knowledge graph comprising millions of nodes and dozens of edge types. It leverages domain-specific foundation models like ESM-2 for proteins and MoLFormer for chemicals to create multi-modal node representations within this graph. A Heterogeneous Graph Transformer (HGT), enhanced with techniques like SignNet positional encodings and virtual nodes, is then pre-trained using multiple self-supervised objectives. When applied to the downstream task of fermentation titer prediction, Canopy's frozen embeddings, even with a simple probe, achieve an R² of 0.41, significantly outperforming traditional tabular baselines (R² = 0.24) and simpler graph neural network variants. This demonstrates Canopy's ability to learn from complex biological relationships and improve strain design.