Canopy把基因、蛋白质、代谢物等生物数据全揉进一个图模型，预测发酵产量比传统方法准了将近一倍，搞合成生物学的可以看看。
Canopy是一个异构图基础模型，整合了10个数据源构建成包含690万个节点（13种类型）和34种边的知识图谱。它使用ESM-2、MoLFormer和PubMedBERT等域专用基础模型编码节点特征，并通过异构图Transformer（HGT）进行预训练。在发酵滴度预测任务中，冻结的Canopy嵌入达到了R²=0.41，远超表格基线（最佳R²=0.24）和同质GNN变体。
Canopy: A Heterograph Foundation Model for Metabolic Engineering
Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering. Existing computational approaches either rely on stoichiometric constraint-based models that cannot learn from experimental data, or apply tabular machine learning to hand-crafted features that discard the relational structure of biological knowledge. We present Canopy, a heterogeneous graph foundation model that integrates ten public and proprietary data sources into a unified knowledge graph (KG) of 6.9M nodes across 13 types and 34 edge types, covering genes, proteins, metabolites, reactions, pathways, strains, and fermentation experiments. Node features are encoded through domain-specific foundation models (ESM-2 for protein sequences, MoLFormer for chemical SMILES, and PubMedBERT for biomedical text), yielding a multi-modal representation within a single graph. We pretrain a Heterogeneous Graph Transformer (HGT) augmented with SignNet positional encodings, Jumping Knowledge aggregation, and virtual nodes using four self-supervised objectives (link prediction, masked node modelling, distance prediction, and contrastive experiment clustering), balanced via learned homoscedastic uncertainty weighting. On the downstream task of fermentation titer prediction, frozen Canopy embeddings achieve $R^{2} = 0.41$ with a lightweight probe, outperforming tabular baselines (best $R^{2} = 0.24$) and homogeneous GNN variants.