Knowledge Graphs for Life Sciences

From Fragmented Data to Connected Intelligence

The pharmaceutical and life sciences industry generates some of the most complex, heterogeneous data in existence: genomic sequences, protein interaction networks, clinical trial records, spontaneous adverse event reports, patent filings, and decades of peer-reviewed literature spanning millions of publications. Historically this data has lived in disconnected silos—laboratory information management systems, electronic health records, regulatory submission databases, and proprietary research platforms that cannot natively communicate with one another. Knowledge graphs provide the integrative semantic layer that transforms these islands of information into a navigable, machine-readable web of biological and clinical knowledge, enabling AI systems to reason across modalities and domains that no single database could span.

Accelerating Drug Discovery and Target Identification

Drug discovery is fundamentally a problem of navigating relationships: which genes are implicated in which diseases, which proteins can be modulated by which compound classes, which biological pathways connect a therapeutic target to a desired clinical outcome. Knowledge graphs encode these relationships explicitly, enabling AI systems to traverse from disease phenotype to molecular mechanism to druggable target in minutes rather than months of manual literature review. The Open Targets platform—a public-private consortium involving EMBL-EBI, the Wellcome Sanger Institute, GSK, Bayer, Pfizer, and Bristol Myers Squibb—has built one of the most influential biomedical knowledge graphs in production, integrating genetic association studies, somatic mutation profiles, RNA expression data, and literature-extracted evidence to systematically score and rank therapeutic targets across hundreds of diseases. AstraZeneca's internal biological knowledge graph, a centerpiece of its AI-driven R&D transformation, connects over 100 million entities spanning genes, diseases, compounds, and clinical outcomes, and now powers target prioritization pipelines that have measurably reduced preclinical attrition rates. In parallel, Bayer's Pharma Research unit deployed a graph-native infrastructure to unify its compound library, assay results, and disease ontologies, enabling researchers to issue cross-domain queries that previously required days of manual data wrangling.

Drug Repurposing and Causal Reasoning

One of the highest-value near-term applications is drug repurposing—identifying approved compounds with established safety profiles that may be efficacious in new indications. BenevolentAI built a proprietary biomedical knowledge graph containing hundreds of millions of nodes and relationships, and in 2019 used graph-based inference to identify baricitinib—a JAK inhibitor approved for rheumatoid arthritis—as a candidate COVID-19 treatment. That prediction was subsequently validated in randomized controlled trials and received FDA Emergency Use Authorization. Healx has applied similar graph-based techniques specifically to rare disease repurposing, where the economics of de novo drug development are prohibitive and patient populations are too small for traditional screening approaches. Causaly, a causal biomedical knowledge graph platform acquired by Elsevier, specializes in distinguishing mechanistic causal relationships from mere statistical associations in the published literature, giving drug discovery teams a more reliable substrate for hypothesis generation than correlation-based mining alone.

Clinical Development, Safety, and Pharmacovigilance

Knowledge graphs have become critical infrastructure in post-market surveillance and pharmacovigilance. Adverse drug event signals are notoriously difficult to detect because they are distributed across spontaneous reporting systems like FDA FAERS, electronic health records, insurance claims databases, and increasingly social media platforms. Graph-based approaches unify patient demographics, comorbidities, concomitant medications, and observed adverse events into a single queryable structure that enables signal detection at a level of clinical specificity that traditional disproportionality analysis cannot achieve. In clinical development, knowledge graphs power patient stratification and trial matching: by encoding inclusion and exclusion criteria as graph queries against patient record graphs, systems can identify eligible cohorts in near real time rather than over weeks of manual chart review. Roche's Flatiron Health subsidiary and Novartis have both invested substantially in graph-based clinical intelligence platforms to accelerate enrollment for oncology trials, where precise molecular subtype targeting is essential and eligible populations are small.

Precision Medicine and Multi-Omics Integration

The convergence of large-scale genomics, proteomics, metabolomics, and longitudinal electronic health records has created a precision medicine data challenge that knowledge graphs are uniquely suited to address. Recursion Pharmaceuticals has built a multi-modal knowledge graph connecting phenotypic imaging data from millions of cellular experiments, transcriptomic perturbation profiles, and chemical structure space, enabling foundation models to learn biologically grounded compound-disease representations that substantially outperform any single-modality approach. Insilico Medicine uses knowledge graph-informed generative AI to propose novel target hypotheses and corresponding lead molecules simultaneously, a workflow that produced a clinical-stage fibrosis candidate in under 18 months from target identification to IND filing—a timeline that would have taken a decade through conventional methods. The open-source Hetionet project demonstrated that integrating 29 public biological databases into a unified knowledge graph could predict novel drug-disease associations with statistically significant accuracy, establishing the proof of concept that commercial successors now build upon at scale within major academic medical centers and integrated health systems.