Keywords
FT-Transformer
Abstract
Diabetes-related drug candidate identification requires the combination of molecular representation learning, metabolic risk analysis, disease mechanism evidence, and clinical knowledge integration. This topic connects transformer-based compound identification methods with evidence-intensive medical reasoning for chronic disease management. FT-Transformer and hybrid FT-TRF models provide structured approaches for diabetes-related candidate screening, while knowledge graph reasoning supports connections among compounds, pathways, comorbidities, and clinical outcomes. The inclusion of metabolic, exosome-related, oncological, and neurodegenerative evidence reflects the complexity of diabetes as a systemic disease associated with cardiovascular, renal, neurological, and inflammatory pathways. Sparse computational methods also support efficient candidate search across large molecular and biomedical graphs. This direction emphasizes interpretable compound prioritization, cross-disease evidence integration, and computationally scalable biomedical discovery.
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