The Best Biomedical Knowledge Graph Companies

How to Cut Drug Findy Timelines by 75% with a Biomedical Knowledge Graph

What kind of services are available for building a biomedical knowledge graph? Several specialized providers help life sciences organizations transform fragmented data into actionable insights. Here are the leading service models:

Service Model	Key Characteristics	Best For
Large-Scale AI Platform Solutions	Pre-built graphs with AI-powered search and proprietary data integration	Organizations needing rapid deployment and broad, cross-domain insights
Data Inventory & FAIRification Services	Access to 200+ public datasets in RDF format with semantic harmonization	Teams leveraging public data and open-science principles
Custom Curation & Ontology Expertise	Bespoke solutions with deep domain expertise and custom ontology development	Research groups with highly specialized needs and unique data
Cloud-Native Data Platforms	Tools and frameworks for building knowledge graphs on existing data lakes	Enterprises with strong in-house data engineering teams wanting full control

Every year, pharmaceutical companies spend $300 billion on R&D, yet productivity keeps stagnating. Why? The problem isn’t a lack of data, but a deluge of it trapped in disconnected silos. Critical insights are fragmented across countless systems: preclinical data from in-vitro assays sits in one database, clinical records from electronic health records (EHRs) in another, multi-omics data (genomics, proteomics, metabolomics) in specialized repositories, and a constant flood of new findings in unstructured scientific literature and patents. This fragmentation forces research teams into a costly and time-consuming manual integration process. When a team needs to assess a new drug target, they might spend 24 months manually piecing together these fragments. This involves data scientists writing custom scripts to query each source, bioinformaticians struggling to harmonize different data formats, and subject matter experts spending weeks reading papers to validate a single hypothesis.

By the time they assemble a coherent picture, the competitive landscape has shifted, and promising therapeutic opportunities have been missed. This inefficiency is a primary driver of the high failure rates and spiraling costs that plague drug discovery. The core challenge is context: a gene variant is just a string of letters until it’s connected to a protein, a pathway, a disease phenotype, and patient outcomes. Without a unified view, these connections remain invisible.

Biomedical knowledge graphs solve this problem by creating a unified, queryable network that mirrors the complexity of human biology. They connect disparate entities—genes, proteins, diseases, drugs, and clinical data—into a single, coherent structure. Leading platforms have built graphs containing 500 million reliable facts and 70 million directional relationships, allowing researchers to traverse complex biological pathways in seconds. For example, a researcher can ask, “Show me all genes that are highly expressed in tumor microenvironments, are targeted by drugs already in Phase II trials for autoimmune diseases, and have known associations with cardiotoxicity.” Answering this without a knowledge graph is a multi-month research project. With one, it’s a single query that returns a ranked list of candidates with supporting evidence. In one documented case, a therapeutics company used this approach to reduce target assessment time from 24 months to just 6 months, while cutting the cost of generating assessment reports by three times. Other enterprise-grade knowledge graphs contain over 4 billion relationships that power drug discovery for leading pharmaceutical companies.

But here’s the catch: not all knowledge graph services are built the same. The value is not just in having a graph, but in having the right graph for your specific needs. Some providers offer massive pre-built platforms optimized for speed and broad discovery. Others specialize in the meticulous process of making your data FAIR (Findable, Accessible, Interoperable, and Reusable) and connecting it to public datasets. Still others provide bespoke curation services for highly niche research areas. Choosing the wrong model can lead to a wasted investment, a slow and frustrating implementation, or a solution that fails to scale as your research questions evolve.

I’m Maria Chatzou Dunford, CEO and Co-founder of Lifebit, where we’ve spent over a decade building federated data platforms for genomics and biomedical research. Throughout my work with public sector institutions and pharmaceutical organizations, I’ve seen the different types of services available for building a biomedical knowledge graph and how different models fit different organizational needs—from rapid platform deployment to custom ontology development that ensures long-term scientific value.

What is a Biomedical Knowledge Graph and Why Does It Matter for R&D?

Think of a biomedical knowledge graph as a living map of everything we know about human health. Each piece of information—a gene, a protein, a disease, a drug, or a clinical trial—becomes a node on this map. But here’s where it gets interesting: these nodes aren’t isolated dots. They’re connected by edges that describe real, meaningful relationships. “Gene A” links to “Disease B” through an “associated with” edge. “Drug C” connects to “Protein D” through an “inhibits” edge. This interconnected structure is what we call a Biomedical Knowledge Graph.

Why does this matter for your R&D team? Because right now, your most valuable insights are probably trapped. Genomics data lives in one system. Clinical records sit in another. Scientific literature exists in yet another silo. When a researcher needs to evaluate a drug target, they’re forced to manually hunt through dozens of disconnected sources, piecing together fragments like a detective solving a cold case.

Biomedical knowledge graphs demolish these silos. They integrate diverse datasets—from multi-omics data and clinical records to scientific literature from sources like PubMed, UniProt, and DrugBank—into a single, queryable network. Suddenly, patterns that were invisible become obvious. Relationships that would take months to find surface in seconds. This unified view accelerates findy and enables truly data-driven decisions. For more on breaking down data silos, check out our Data Integration Platform: Complete Guide.

The technical foundation relies on standards that make this integration possible. The Resource Description Framework (RDF) structures data using simple subject-predicate-object triples—think of them as sentences that computers can read. For example, a finding in a paper, “Sorafenib inhibits the BRAF kinase,” is deconstructed into a triple: the subject (‘Sorafenib’) is linked by a predicate (‘inhibits’) to an object (‘BRAF kinase’). This simple, powerful format ensures that data from different sources speaks the same language. Crucially, the graph also captures provenance by attaching metadata to this relationship, such as the PubMed ID of the paper, ensuring every fact is traceable to its source.

To query these graphs, researchers use SPARQL, a specialized query language built for navigating complex relationships. It’s like SQL, but designed specifically for graphs where the connections and pathways between data points are as important as the data points themselves.

The semantic backbone comes from ontologies, often defined using the Web Ontology Language (OWL). These are formal vocabularies that define the types of entities (genes, diseases, drugs), their properties, and the rules governing their relationships. The ontology ensures the system understands that ‘Sorafenib’ is a ‘small molecule drug’, ‘BRAF’ is a ‘protein kinase’, and that a ‘drug’ can ‘inhibit’ a ‘protein’. This semantic layer prevents nonsensical connections and allows for sophisticated reasoning, turning a simple database into a true knowledge base.

The Role of AI and Machine Learning

Here’s where biomedical knowledge graphs transform from useful databases into predictive powerhouses. When you combine these interconnected networks with artificial intelligence and machine learning, you open up capabilities that go far beyond simple data retrieval.

Natural Language Processing (NLP) tackles one of research’s biggest challenges: extracting structured information from unstructured text. Scientific papers, clinical notes, and research abstracts contain countless insights, but they’re written in human language. NLP algorithms read these texts in a two-step process. First, Named Entity Recognition (NER) identifies mentions of relevant concepts like gene names, drug compounds, or disease terms. Second, Relation Extraction (RE) analyzes the sentence’s syntax and semantics to identify the relationship between them. This allows the system to automatically extract a fact like “drug X inhibits protein Y” and add it to the graph, continuously populating it with the latest findings.

Knowledge Graph Embeddings (KGEs) translate the graph into a format that machines can truly understand. These techniques convert entities and relationships into numerical vectors in a high-dimensional space, where the position and direction of the vectors represent their meaning and connections. This enables link prediction, where the system identifies potential new relationships that aren’t explicitly stated but can be inferred from existing patterns. Imagine the graph knows that many drugs similar to Drug A successfully treat Disease X by targeting Pathway Y. A KGE model can learn this pattern and predict that a new, structurally similar Drug B might also treat Disease X, generating a novel, testable hypothesis. Scientific research on KGEs in biomedicine shows how these techniques are revolutionizing drug findy.

Graph Neural Networks (GNNs) take this further by learning directly from the graph’s structure. Unlike other models, GNNs operate on the graph itself, analyzing the ‘neighborhood’ of each node to learn its function and context. They can cluster diseases with similar molecular footprints, classify proteins to identify novel drug targets, and recommend personalized treatments by comparing a patient’s genomic profile to similar profiles in the graph. By recognizing complex patterns across millions of connections, these AI models generate hypotheses that would be impossible for humans to spot manually. This is the technology powering advances in AI for Genomics and beyond.

Key Benefits for Research and Development

The question of how to build a biomedical knowledge graph matters because the benefits are transformative—but only if you choose the right implementation partner and model.

Accelerated findy tops the list. For example, one leading service provider helped a therapeutics company reduce target-indication assessment time from 24 months to just 6 months using a knowledge graph. That’s an 18-month head start over competitors still drowning in data silos. Imagine launching clinical trials a year and a half earlier.

Reduced costs follow naturally from faster timelines. The same project cut costs for generating custom target reports by three times. When data scientists can focus on analyzing results instead of wrangling data from disparate sources, R&D efficiency skyrockets. This frees up valuable resources to be allocated to more innovative research.

Increased success probability might be the most compelling benefit. Early drug findy is notoriously inefficient—most candidates fail. By enabling a more systematic and evidence-based approach to target selection and validation, a well-constructed knowledge graph has been shown to improve success probability from a dismal 1:2000 to an impressive 1:5. That’s a fundamental shift in R&D efficiency.

Data-driven decisions and hypothesis generation become part of your daily workflow. Researchers can ask complex questions that span multiple data types: “Show me all genes associated with inflammatory pathways that are targeted by FDA-approved drugs for other indications.” The graph returns answers in seconds, complete with supporting evidence and confidence scores. This empowers the kind of systematic, evidence-based findy that drives AI-Driven Drug Discovery forward.

Accelerated Drug Repurposing is another major benefit. By mapping the mechanisms of action for thousands of existing drugs against the molecular basis of thousands of diseases, KGs can uncover unexpected therapeutic uses. A drug approved for rheumatoid arthritis might be found to modulate a pathway also implicated in Alzheimer’s disease. These predictions, based on shared genes, proteins, or pathways, can shave years and billions of dollars off the development pipeline by starting with compounds that already have established safety profiles.

Which Service Models Exist for Building a Biomedical Knowledge Graph?

What are the different approaches to building a biomedical knowledge graph? This is one of the most common questions we hear from research teams ready to transform their data chaos into actionable insights. The answer isn’t straightforward—because not every organization needs the same solution. A pharmaceutical giant exploring hundreds of drug targets has different requirements than a niche research group studying rare diseases, and both differ from a clinical team optimizing patient stratification.

We’ve identified three primary service models, each designed for specific research scenarios and organizational maturity levels. Think of these as different paths up the same mountain—they all reach the summit, but the route you choose depends on your starting point, timeline, and what you’re carrying with you.

Model 1: Large-Scale AI Platform Solutions

Large-scale AI platform solutions provide pre-built biomedical knowledge graphs that are ready to query from day one. These platforms have already done the heavy lifting—ingesting, cleaning, and connecting data from thousands of sources into massive, interconnected networks. We’re talking about graphs containing 500 million reliable facts and billions of relationships that span genes, proteins, diseases, drugs, clinical trials, and scientific literature.

The defining feature of these platforms is their AI-powered search capabilities that go far beyond simple keyword matching. Advanced natural language processing allows researchers to ask complex questions in plain language, while machine learning models distinguish between causality and correlation—a critical distinction when you’re trying to understand whether a drug causes a therapeutic effect or is merely associated with it in the literature.

These platforms continuously integrate proprietary data alongside public sources. They monitor scientific publications in real-time, extract new findings, and update the knowledge graph automatically. For a pharmaceutical company assessing multiple drug targets simultaneously, this means always working with the most current evidence without manual literature reviews. Learn more about how AI accelerates this process in our AI Drug Discovery Platform article.

Implementation and Onboarding: Adopting such a platform is a structured process. It begins with a scoping phase to identify high-priority research questions. This is followed by data integration, where connectors are configured to ingest the client’s proprietary data (e.g., internal assay results) into a secure, private instance of the graph. User training is critical, focusing on how to formulate effective scientific questions that leverage the graph’s full power. The real advantage is scalability and speed. Large-scale platforms serve as a single source of truth for your entire research organization. Multiple teams can run complex queries simultaneously without performance issues. Some platforms contain over 4 billion relationships, enabling researchers to explore connections across therapeutic areas that would take years to map manually. Others boast 70 million directional relationships with explicit causality markers, dramatically improving the quality of insights for drug findy and repurposing.

Limitations: The primary trade-off is a lack of deep customizability. If your research requires a novel type of relationship not present in the platform’s ontology, you may be unable to add it. You are also dependent on the vendor’s roadmap for new features and data integrations. The AI-driven insights can also sometimes feel like a ‘black box,’ making it difficult to trace the exact evidence chain for a given prediction, which can be a hurdle for regulatory submissions.

Model 2: Data Inventory & FAIRification Services

If your research relies heavily on public biomedical data, data inventory and FAIRification services offer a compelling alternative. These providers specialize in making public datasets Findable, Accessible, Interoperable, and Reusable—the core principles known as FAIR data.

The value proposition is straightforward: instead of spending months tracking down datasets, negotiating access, and writing custom parsers for dozens of different formats, you get 200+ public datasets already transformed into a standardized RDF format. This includes foundational resources like the Gene Ontology (GO) for gene function, ClinVar for genomic variation, TCGA for cancer genomics, drug-centric databases like DrugBank and ChEMBL, and clinical terminologies like SNOMED CT and ICD-10.

The magic happens through semantic harmonization. These services don’t just dump data into a common format—they map concepts across datasets using shared ontologies like the UMLS (Unified Medical Language System). A “myocardial infarction” in one database becomes properly linked to “heart attack” in another, and both connect to the relevant gene variants and drug mechanisms. This ontology mapping creates a coherent knowledge graph from inherently fragmented sources.

The FAIRification Workflow: This is a sophisticated ETL (Extract, Transform, Load) pipeline. Extraction involves automated scripts pulling data from public APIs and FTP sites. Transformation is the critical step where data is cleaned, normalized, and mapped to a central ontology to resolve semantic ambiguity. Loading involves converting the harmonized data into RDF triples and indexing it in a high-performance graph database (a ‘triple store’) accessible via a SPARQL endpoint. For research teams building on open-science foundations, this model provides comprehensive coverage without the cost of licensing proprietary databases. To understand how harmonization works at scale, check out our guide on AI for Data Harmonization.

Limitations: These services focus primarily on public data. If your competitive advantage comes from proprietary datasets—internal genomic studies, clinical trial results, or real-world evidence—you’ll need additional integration work. This often involves building your own internal knowledge graph and setting up a federated query system to analyze both public and private data together.

Model 3: Custom Curation & Ontology Expertise

Sometimes, off-the-shelf solutions don’t cut it. When you’re working in a highly specialized therapeutic area, dealing with complex proprietary data, or need precise control over how relationships are defined, custom curation and ontology expertise becomes essential.

These providers offer bespoke knowledge graph solutions built specifically for your research questions. The process starts with deep domain expertise—subject matter experts (SMEs) who understand not just the technology but the science itself. They work with your team to design a custom ontology that captures the nuances of your specific domain.

Manual curation services are a key differentiator. Rather than relying solely on automated extraction, human experts review literature, validate relationships, and ensure the knowledge graph reflects current scientific understanding. This level of quality control is crucial when decisions worth millions of dollars ride on the accuracy of a drug-target relationship.

A Deeper Dive – Custom Case Study: Consider a biotech firm specializing in Amyotrophic Lateral Sclerosis (ALS). A generic graph might link the SOD1 gene to ALS. A custom-built graph would go deeper. The ontology could distinguish between different SOD1 mutations (e.g., A4V vs. G93A), link them to specific disease progression rates from the company’s own clinical data, and connect them to cellular mechanisms like protein misfolding, all curated from the latest literature. This allows researchers to ask hyper-specific questions like, “Which of our proprietary compounds have shown efficacy in cell lines with the SOD1 A4V mutation and also modulate mitochondrial pathways?” This precision is impossible with generic solutions.

Team and Maintenance: This model is a collaborative partnership. The vendor provides ontologists and bio-curators, but success hinges on active participation from the client’s internal SMEs, data engineers, and data scientists. Long-term maintenance is also critical. A service-level agreement (SLA) should be established for ongoing curation to ensure the graph is updated as the science evolves. The trade-off is time and cost. Building a custom knowledge graph takes months, not weeks. But when your competitive advantage depends on insights that no one else has, custom curation delivers value that generic solutions simply cannot match.