A-S.B.06: Graph-based Modeling of Microbe–Drug Interaction Networks using MASI
Keywords
Microbiome–Drug Interactions, Heterogeneous Graph Learning, Pharmacomicrobiomics
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Graph-based Modeling of Microbe–Drug Interaction Networks using MASI
Microbial interactions with therapeutic compounds are central to pharmacomicrobiomics and precision medicine. Although MASI provides curated microbe–substance and microbe–disease associations, computational modeling of these heterogeneous relationships under realistic generalization settings remains limited.
We constructed a heterogeneous graph integrating microbe–substance and microbe–disease associations from MASI, enriched with microbial taxonomic annotations, probiotic/abundance indicators, and substance category features. We employed a Heterogeneous Graph Transformer (HGT) to model multi-relational interactions across microbial taxa, substances, and diseases and to learn relation-aware node embeddings. To reduce optimistic bias, we adopted a leave- microbe-out evaluation strategy, ensuring that all interactions of held-out microbial taxa were excluded during training. Additionally, we applied degree-biased hard negative sampling to simulate realistic link prediction scenarios. Model robustness was evaluated across five random seeds. Under leave-microbe-out evaluation with hard negatives (1:1 ratio), the model achieved ROC- AUC = 0.9026 ± 0.0240 and PR-AUC = 0.8692 ± 0.0321, demonstrating strong generalization to previously unseen microbial taxa. The model identified several high-confidence novel candidates, including predicted associations between Roseburia hominis and anticholinergic agents such as dicyclomine hydrochloride, as well as between Streptococcus thermophilus and dicyclomine. While direct experimental validation is limited, existing studies suggest that antispasmodic and anticholinergic compounds can modulate gut microbial composition, supporting the biological plausibility of these predictions. This heterogeneous graph-based framework enables robust and unbiased discovery of candidate microbe–drug interactions and provides a scalable computational foundation for hypothesis generation in pharmacomicrobiomics.Contact Attendee
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