Biochemically Constrained Multi-Omics Integration Reveals Protein-Metabolite Dependencies Across Diseases.

Integrating proteomic and metabolomic data is essential for understanding complex diseases, yet current approaches that rely primarily on statistical associations often overlook the structured biochemical relationships between molecular entities and suffer from discriminative instability in small clinical cohorts. Here, we present ProMetNet, a biochemically constrained framework that incorporates pathway-derived connectivity from the Reactome database into neural network architecture. By encoding protein-metabolite relationships based on reaction topology, ProMetNet models structured cross-omics dependencies rather than relying solely on statistical correlations, reducing spurious associations while preserving global molecular context and improving robustness in data-limited settings. Across four heterogeneous disease cohorts, including Alzheimer's disease, type 2 diabetes, COVID-19, and glioblastoma, ProMetNet consistently outperforms evaluated multi-omics integration methods, including MOGONET, P-NET, PEARL, and MOINER, maintaining high discriminative performance under substantial data downsampling. In addition to classification accuracy, the framework prioritizes biologically plausible protein-metabolite dependencies that are not captured by conventional differential or correlation-based analyses. Importantly, pathway-level signals identified by ProMetNet demonstrate consistent discriminative performance in independent large-scale population data from the UK Biobank (N = 47,507), supporting their robustness and generalizability. Together, these results establish ProMetNet as a biologically grounded and interpretable framework for multi-omics integration, enabling robust identification of structured molecular dependencies across diseases.