Drug Discovery
Ontology-Driven Target Identification in Early Drug Discovery
Target identification — the process of selecting the molecular target most likely to yield a safe and effective drug for a specific disease — is one of the highest-stakes decisions in pharmaceutical development. Knowledge graphs that integrate genetics, proteomics, disease biology, and clinical evidence provide a structured framework for making this decision with less uncertainty.
Drug Discovery
Drug Repurposing Using Indication Knowledge Graphs
Drug repurposing — identifying new therapeutic uses for existing compounds — is the most efficient path to clinical proof of concept because the safety profile is already established. Indication knowledge graphs enable systematic, data-driven repurposing hypothesis generation at a scale that cannot be achieved through literature review alone.
Drug Discovery
Semantic Integration of Genomics, Proteomics, and Clinical Data
The integration of genomics, proteomics, transcriptomics, and clinical data into a unified analytical framework is the technical foundation of precision medicine drug discovery. Without a semantic layer that defines how concepts from each data modality relate to each other, multi-omics integration produces noise rather than insight.
Drug Discovery
Knowledge Graphs for Biomarker Discovery
Biomarker discovery — identifying molecular features that predict disease risk, progression, or treatment response — is one of the most knowledge-intensive activities in pharmaceutical research. Knowledge graphs that formalise the relationships between molecular entities, disease biology, and clinical outcomes dramatically accelerate hypothesis generation.
Drug Discovery
Unifying Preclinical and Clinical Knowledge Through Ontologies
The translation gap between preclinical and clinical drug development — where efficacy signals in animal models fail to predict human efficacy — is partly a knowledge gap. Ontologies that formally align preclinical biological concepts with their clinical counterparts reduce this gap by making translational comparisons systematic rather than ad hoc.