Executive Summary

In the rapidly evolving field of biotechnology and drug discovery, integrating diverse data types—chemical compounds, biological assays, molecular targets, and therapeutic outcomes—is paramount. This report details the architectural patterns and knowledge representation strategies underpinning comprehensive biotech knowledge graphs that semantically link compounds, assays, targets, and outcomes. A knowledge graph (KG) is a flexible data model that represents entities (e.g., molecules, proteins, diseases) as nodes and their relationships (e.g., binds, assays, causes) as edges (^[1]) (^[2]). By leveraging standards such as RDF and OWL and by mapping heterogeneous datasets into a unified framework, knowledge graphs can integrate and contextualize data from disparate sources (e.g., ChEMBL, PubChem, UniProt, Gene Ontology, BioAssay Ontology) (^[3]) (^[4]). This enables sophisticated querying and inference that traditional relational models cannot easily support (^[5]) (^[6]).

This report provides an in-depth analysis of the architecture patterns for constructing such KGs in biotech discovery. Key data integration patterns—Extract-Transform-Load (ETL), Extract-Load-Transform (ELT), replication (via streaming or change feeds), and virtualization—are examined with respect to their suitability for scientific data pipelines, as outlined in knowledge-graph platforms (^[7]) (^[8]). We review ontology- and identifier-based linking strategies (e.g., using persistent URIs, InChIKeys, bio-ontologies) to reconcile semantics across datasets (^[9]) (^[4]). Case studies illustrate how integrated knowledge graphs accelerate discovery: the Open PHACTSplatform examples of linking drug-target-pathway data (^[10]) (^[3]); the FORUM metabolomics KG for chemical–disease associations (^[11]) (^[2]); Chem2Bio2RDF for chemogenomics (^[2]); and Knowledge4COVID-19, which integrated clinical and literature data to analyze drug interactions and toxicities (^[12]) (^[9]). These illustrate how KGs enable advanced queries and machine reasoning (e.g., link prediction, graph embeddings) to reveal hidden relationships and predict outcomes (^[13]) (^[14]).

Finally, we discuss challenges (data heterogeneity, licensing, provenance, scalability) and future directions—such as FAIR principles, distributed architectures, and AI-enhanced graph analytics—that will shape next-generation discovery platforms (^[5]) (^[15]). In sum, biotech knowledge graphs, underpinned by robust integration architectures, offer a powerful framework to connect compounds, assays, targets, and outcomes, thereby transforming data lakes into actionable semantic networks that drive innovation in drug discovery.

Introduction

Modern drug discovery and biotechnology generate vast quantities of data across multiple domains: chemical structures, high-throughput assay results, molecular targets, pathway interactions, clinical outcomes, and more. However, these data are often siloed in domain-specific repositories, each with its own formats, identifiers, and semantics (^[16]) (^[2]). For example, medicinal chemistry data may reside in ChEMBL or PubChem BioAssays, protein targets are catalogued in UniProt or the Protein Data Bank, pathway annotations come from Reactome or WikiPathways, and phenotypic outcomes appear in literature or clinical registries. Integrating such heterogeneous data is crucial to discover novel relationships—for instance, identifying a new therapeutic use for a compound (drug repurposing) or elucidating how in vitro assay outcomes translate to in vivo efficacy.

A biotech knowledge graph offers a coherent data model to bridge these domains. In a KG, entities like compounds, assays, targets, diseases, and side-effects are nodes, while relationships such as "compound binds target", "assay measures outcome", or "target involved in pathway" are edges (^[2]) (^[6]). By encoding data in RDF triples or a graph database, and linking via shared identifiers (URIs), ontologies, or mappings, one can query the integrated graph semantically, reason over it, and apply graph algorithms. As noted by Miles et al., knowledge graphs provide “a semantic layer that models real-world entities (drugs, diseases, genes, patients) and their multi-dimensional relationships” (^[17]), enabling complex tasks such as context-aware search, hypothesis generation for drug discovery, and patient stratification.

Building such a KG involves multiple stages: data acquisition and integration (ingesting raw data from sources), normalization and semantic harmonization (mapping synonyms, enforcing ontologies), storage in a graph database (e.g. Neo4j, RDF triple store), and query/analysis interfaces (SPARQL, GraphQL, APIs). This report examines architecture patterns that guide these stages, particularly in the context of drug discovery (the “discovery data integration” pipeline). We cover the current state of knowledge graph use in biomedicine (including major projects and platforms), analyze specific architectural best practices (ETL vs virtualization, identifier mapping, ontology use), and present case studies (both public initiatives and research prototypes) that illustrate practical implementations. We emphasize evidence-based discussion, drawing on statistics (e.g. </current_article_content>KG sizes), expert insights, and concrete examples from literature (^[3]) (^[6]). Finally, we discuss future directions—how advancements like FAIR data practices, machine reasoning, and cloud-native architectures will shape the next generation of biotech knowledge graphs.

Data Sources and Ontologies in Drug Discovery

Integrating compounds, assays, targets, and outcomes requires drawing from many specialized databases and ontologies. Below is a non-exhaustive summary of key resources and standards:

For example, the Open PHACTS platform integrates many of these: it federates ChEMBL, ChEBI, DrugBank, ChemSpider, UniProt, GO, WikiPathways, etc., making them queryable through a unified SPARQL API (^[3]). The BioKG project likewise gathers UniProt, Reactome, OMIM, GO, etc., to build a KG of biomedical relationships (^[19]). Table 1 (above) outlines such data sources and their content. Note that many resources are open-access or have well-defined licensing (e.g., CC-BY), while proprietary data (e.g. Thomson Reuters Integrity) may require special handling (^[20]).

In addition to databases, ontologies provide the semantic backbone. For compounds, ChEBI is an ontology of chemical entities; for targets, Gene Ontology (GO) describes protein functions; for diseases, DOID or MeSH provide classification 母. Critically, the BioAssay Ontology (BAO) offers a controlled vocabulary to describe assay formats and endpoints (^[4]). BAO formalizes terms like “IC50” or assay technologies, enabling cross-assay comparisons that raw data labels do not support (^[4]) (^[6]). For instance, PubChem’s free-text assay endpoints (with 17,000+ unique labels) become queryable once mapped to BAO’s canonical terms (^[4]) (^[6]). These ontologies (often in OWL/RDF form) can be loaded into the KG to provide conceptual consistency: e.g., linking the label “Metformin” in one dataset and “Glucophage” in another to the same Drug URI via RxNorm or ChEBI mappings (^[21]).

An important integration tool is identifier mapping. Chemical compounds may have different IDs across databases (InChIKey, PubChem CID, ChEMBL ID, etc.); UniChem and chemical registries can normalize these. Similarly, targets are reconciled via UniProt accession numbers or HGNC gene symbols. Persistent URIs (e.g. based on recognized namespaces) ensure that the same entity in disparate sources is unified in the graph (^[21]) (^[2]). This normalization is essential to avoid duplicate nodes – for example, collapsing “Metformin (CID:4091)” and “Glucophage” to one entity in the KG. The FAIR data principles emphasize such use of global, persistent identifiers (e.g. DOIs, IRIs) to ensure interoperability (^[9]) (^[22]). Many KG architectures rely on generating or adopting stable IRIs for each class and instance to satisfy these requirements.

Architecture Patterns for Data Integration

Constructing a biotech knowledge graph from heterogeneous discovery data involves choosing an overall architecture and integration pattern. Here we discuss the main patterns identified in practice (^[7]) (^[8]), and how they apply to discovery pipelines.

Batch vs Streaming Integration

• Batch “Load” pattern (RDF replace): If source data can be exported in RDF (or easily transformed offline), the simplest approach is to load or replace the graph wholesale. For example, if ChEMBL provides SPARQL dumps, one can drop the old named graph and bulk-upload new triples (^[23]). This Graph Replace approach is straightforward but locks the graph during loads and scales poorly for very large graphs (^[24]). It works best when updates are infrequent and data size is manageable (e.g. nightly or weekly full loads). In practice, one might script a cyclical ETL job: extract new data from source, transform to RDF using tools (see below), then delete+insert the graph (or use graph-optimized replace as in GraphDB) (^[23]).

• ETL (Extract–Transform–Load): This is the classic pattern when source data are not in RDF format (e.g. relational tables, CSV, JSON). Data is first extracted from the source, transformed into the target graph schema (mapping to triples and URIs), then loaded into the graph store. Such ETL can be done with tools or custom scripts. For example, one might use Ontotext Refine or Apache NiFi to normalize chemoinformatics tables into RDF (^[25]). In ETL, it is crucial to generate persistent identifiers (URIs) during transform, so nodes remain stable on reload (^[26]). The advantage of ETL is that one can apply complex transformations (e.g. canonicalizing strings to ontology terms) outside the graph. Its disadvantage is that any update requires re-running the whole pipeline.

• ELT (Extract–Load–Transform): A variant where the raw data (already in some RDF form) is first loaded into a staging graph, then transformed in situ using queries (e.g. SPARQL UPDATE) to achieve the final schema (^[27]). This is useful when dealing with multiple RDF sources whose ontologies differ. For instance, one could load UniProt RDF and GO RDF into a temp graph, then run SPARQL/OWL alignment to interlink them. Ontotext’s blog notes that ELT is handy when transformations depend on relationships only available in the graph (^[27]). The trade-off is more complex queries and the necessity to maintain provenance (via named graphs for each source step).

• Combined ETL+ELT (“ETLT”): In practice, pipelines often use ETL for initial heavy lifting and then ELT for fine-tuning. For complex link discovery (e.g. merging ontologically-similar nodes), a data engineer might first ingest sources via ETL, then run semantic reconciliation (instance matching, schema alignment) within the graph (^[27]) (^[28]).

• Streaming/Replication (Upstream and Downstream): For high-frequency updates or real-time data, graph builders may use message brokers (e.g. Kafka). In the upstream replication pattern, each source system emits change events (new compounds, assay results). A connector (e.g. GraphDB Kafka Sink (^[29])) listens and applies those inserts/deletes to the graph continuously (^[30]). This avoids large batch updates and keeps the KG near-live. Conversely, downstream replication allows the KG to push updates to other systems (e.g. feeding a search index) via Kafka (^[31]). These patterns require that sources provide streaming change logs. In biotech R&D, some internal databases might support this (e.g. whenever a new assay result is published). It’s an advanced pattern enabling low-latency integration, at the cost of architectural complexity.

• Data Virtualization (Federation): Instead of copying data, one can leave source DBs in place and create a virtual KG via mappings. Using RDB-to-RDF mapping languages (R2RML/RML or vendors’ OBDA tools), the KG system can translate SPARQL queries at runtime into SQL (or API calls) against the original databases (^[8]). For example, a virtual RDF repository might point to a ChEMBL PostgreSQL via an RML file. Upon query, it fetches the latest data on the fly. Ontotext’s GraphDB uses the ONTOP engine to support 20+ sources in virtual mode (^[8]). The main advantage is instant synchronization and no ETL, which is appealing for extremely dynamic or sensitive data. The drawbacks include rigid queries (limited by SQL features of source) and potentially poor performance on large joins (^[28]). Virtualization requires sources to have clean, normalized schemas and stable identifiers; otherwise, each query may incur heavy translation cost.

In practice, many discovery KGs use a hybrid approach. Core, relatively static data (e.g. chemical structures, gene names) can be loaded in bulk, while dynamic experimental results (new assays) might be left virtual or streamed. A data lake landing zone often receives raw files (CSV, JSON) which are then ETL’d nightly into RDF; concurrently, certain legacy systems may be exposed via virtual endpoints. The key is to match each data type with an optimal pattern (Table 2).

Table 2: Data integration patterns for knowledge graph construction

The choice of pattern also interacts with architecture layers. A common pattern is to separate data landing, semantic integration, and service layers. For instance, raw data from assays might first land in a data lake (raw files), then undergo ETL to produce RDF triples stored in a graph database. At the service layer, applications access the KG via SPARQL endpoints, GraphQL APIs, or visualization tools. Some architectures treat the graph as the central “hub” in a service-oriented design: back-end microservices ingest and map data from each domain into the hub, while front-end components query the hub for analytics.

Notably, mapping and harmonization often precede or accompany integration. Techniques like Named Entity Recognition and Linking (NER/NEL) can annotate literature or unstructured data to link them into the KG (^[32]) (^[33]). In the Knowledge4COVID-19 project, for example, mapping rules defined in RDF Mapping Language (RML) were applied to unify disparate COVID-19 knowledge sources into one KG (^[9]). This declarative mapping layer is an important architecture concept: rather than hard-coding transforms, RML rules allow defining how each data table or JSON feed is turned into RDF triples via a unified schema (^[9]). In summary, integrating discovery data relies on combining ETL and semantic mappings, underpinned by robust data pipeline design.

Semantic Modeling and Ontologies

Building a knowledge graph is not only about connecting data, but also about modeling knowledge. A clear ontology/schema defines the entity types and relationships in the graph. In biotechnology, one often employs a domain ontology or schema that captures the key concepts: Compound, Assay, Target, Disease, Outcome, etc. For instance, Hetionet defines 11 entity types (genes, diseases, compounds, symptoms, pathways, etc.) and 24 relation types (^[34]). The choice of schema influences what queries are easy to express.

Crucial modeling elements include:

• Compound and Target classification: Sub-classes for small molecules (e.g. SmallMolecule vs ChemicalEntity) and targets (genes, proteins). Persistent identifiers (ChEMBL ID, PubChem CID, UniProt accession) are used as unique node IDs (^[35]). Chemical structure may be stored as an InChIKey attribute.

• Assay ontology: Using BAO, assays are categorized by format (cell-based, biochemical, reporter gene, etc.) and endpoint type. A normalized “IC50” concept in BAO ensures that queries for assays measuring IC50 capture all semantically equivalent results (^[4]). The ontology also defines relationships like has_assay_format, has_qc_criteria, enabling semantic queries beyond raw text.

• Outcome representation: Outcomes may be quantified (e.g. numeric potency) or categorical (active/inactive). When loading assay results, one maps result columns to ontology terms (e.g. bao:IC50 for potency, bao:PercentInhibition for percent effect). For phenotype outcomes (e.g. patient response), controlled vocabularies like SNOMED CT or MedDRA might be linked. The outcome entity could be an OWL class with attributes for value, units, and significance.

• Relationships: Link patterns include compound_has_target, compound_tested_in_assay, assay_measures_activity_on, target_associated_with_disease, compound_treats_disease, etc. Many KGs reuse predicates from widely-used schemas: FOAF, DCAT, Dublin Core for generic properties, and specialized ones like SIO (Semanticscience Integrated Ontology) or BioPAX for pathways. As an example, the Pharos schema defines edges for drug-target interaction, protein-protein interaction, gene-disease association, etc. (^[36]) (^[36]). Having a consistent ontology allows federating queries; e.g., one could query “all compounds that bind targets involved in kinase signaling pathways” and rely on the KG to know how to traverse compound–target and target–pathway edges semantic aligment.

• Provenance: For research KGs, tracking provenance (source dataset, publication, timestamp) is often encoded via reification or named graphs. This allows users to trust or filter assertions. For instance, Open PHACTS retains indicators of which original database each triple came from (^[16]).

Overall, the semantic model must balance expressiveness with performance. Overly complex OWL expressivity can slow queries, whereas too flat a model loses inferencing power. Many successful KGs (Hetionet, BioKG) use a “moderately rich” schema: classes and properties primarily in OWL/RDF (possibly some class hierarchies), enabling inference of certain transitive facts (e.g. subclass inference, pathway membership) (^[2]) (^[6]).

Case Study: Open PHACTS Discovery Platform

One of the pioneering efforts in applying semantic integration to drug discovery was the Open PHACTS project (2011–2014), an IMI initiative to build a public knowledge graph of pharmacological data (^[10]). Open PHACTS served as a real-world case of linking compounds, targets, and pathways to address drug discovery questions. The architecture and use cases highlight both the technical approach and scientific payoff.

Open PHACTS ingested data from diverse sources: ChEMBL, ChEBI, DrugBank, ChemSpider, UniProt, GO, WikiPathways, Enzyme, and others (^[3]). Each source was mapped into a common RDF schema with unified identifiers (URIs). For example, all targets were identified by UniProt URIs, and compounds by standardized InChIKey-based URIs (via ChEMBL or ChemSpider IDs). The integration was accomplished using ETL-style processing with custom mappings, resulting in a large triple store.

With the integrated KG loaded into a triple store (OpenLink Virtuoso), Open PHACTS provided a SPARQL endpoint and, notably, domain-specific REST APIs for common queries (^[20]). Researchers authored scientific workflows (in KNIME and Pipeline Pilot) that queried Open PHACTS for multi-step questions. For instance, one use case was: “Find all chemical compounds active on targets involved in the ErbB signaling pathway, which have implication in disease X.” The workflow identified targets in WikiPathways, found compounds bound to those targets via ChEMBL/DrugBank, and filtered by disease association (from DisGeNET or OMIM) (^[37]) (^[3]).

Key insights from Open PHACTS included:

• Provenance and semantic interoperability: They cataloged licensing and data provenance meticulously, aligning licenses to allow integration (^[3]). They also had to harmonize identifiers across sources, which motivates modern emphasis on persistent URIs.
• APIs + tooling: Beyond raw SPARQL, the development of an intuitive API (e.g. “compound → targets” service) and client libraries (Python, Java) made the platform accessible to chemists and biologists without deep SPARQL knowledge (^[20]).
• Case outcomes: The use-case demonstrations showed that linking disparate data simplified tasks like target validation for phenotypic screens and compounds exploration. While quantifying impact is hard, Open PHACTS became widely cited, and its approach informed later efforts (its concepts underpin parts of the newer Open PACTS, and its query patterns are referenced in literature).

In summary, Open PHACTS exemplifies a complete KG architecture: data sources → integration → RDF graph → query services → user workflows. Its success underscores several principles: use well-defined identifiers, integrate high-quality curated databases, and focus on user-friendly access methods. While some components (like publication of public SPARQL under open licenses) were specific to its IMI context, the architectural lessons (semantic federation, workflow integration) remain highly relevant (^[10]) (^[3]).

Case Study: Chem2Bio2RDF

An even earlier example (pre-dating Open PHACTS) is Chem2Bio2RDF (2010), a Semantic Web framework integrating chemogenomic data (^[2]). Chem2Bio2RDF focused on linking chemical and biological data for systems chemical biology. The project aggregated numerous datasets (DrugBank, KEGG, PDB, GO, OMIM, pathway and side-effect repositories) into one repository that cross-links to Bio2RDF and LODD (^[38]). It extended SPARQL with cheminformatics functions to handle chemical queries.

In the KG, compounds come from multiple sources, each mapped to URIs; targets are proteins/genes; diseases and pathways are linked. Importantly, Chem2Bio2RDF introduced a linked-path generation tool to help researchers formulate SPARQL queries that traverse compound–target–pathway–disease chains (^[38]). This illustrates how complex queries become feasible once data is semantically integrated: for example, they could trace a “polypharmacology” path by following a drug through several target interactions to side-effects and pathways (Figure 1 in [42]) (^[2]).

As a research project, Chem2Bio2RDF provided insights into how much manual curation is needed. They reported that needed data sources often contain similar information in different formats, and many overlaps exist (^[2]). Harmonizing these involved handling different ontologies and semantics—a microcosm of drug discovery data silos. They also quantified the KG: e.g., it contained millions of triples linking compounds (~1 million), proteins (~70k), pathways, and side-effects (^[2]).

The project’s outcomes included demonstration analyses: identifying all proteins that a given compound might “polypharmacologically” affect by walking the KG (^[2]). It also associated adverse drug reactions to pathways via graph traversal. While not a production system, Chem2Bio2RDF’s significance lies in proving the concept and highlighting challenges of large-scale RDF integration in chem-bio domains (^[38]) (^[2]).

Case Study: FORUM Knowledge Graph (Metabolomics)

FORUM is a more recent example (2021) focusing on metabolomics and metabolite–disease links (^[11]). In metabolomics, researchers measure hundreds of small molecules (metabolites) and seek to interpret them in biological context. FORUM built a KG connecting chemicals and biomedical concepts by federating multiple databases and the scientific literature (^[11]).

Key features of FORUM:

• Data sources: It extracts associations from public DBs (e.g., PubChem, MeSH) and co-occurrence in literature. Compounds (via PubChem CID) are linked to MeSH disease terms through shared mentions and curated links.
• Semantic enrichment: FORUM uses ontologies (ChEBI for chemical classification, MeSH for disease hierarchy) so that reasoning can infer new relationships (^[39]). For example, if compound X is linked to a broad disease category, it infers links to its subtypes.
• RDF results and endpoint: The resulting KG triples (including inferred ones) are available via a SPARQL endpoint (^[11]), supporting queries like “what diseases are enriched in metabolite signatures for condition Y?”. The team performed enrichment analyses to validate each extracted edge.

Importantly, FORUM demonstrates how combining structured DBs and text mining in a KG adds value. By using semantic webs stacks, one can infer that a metabolite is related to a disease even if not explicitly stated in one source, by traversing the graph. The authors indicate that the KG facilitates hypothesis generation: e.g. suggesting new disease biomarkers. While FORUM is application-specific, it shows how KG architecture (federated data + ontology + reasoning) can drive discovery in omics (^[11]) (^[39]).

Case Study: Knowledge4COVID-19 (COVID Treatment Toxicities)

The Knowledge4COVID-19 project (2022) merged disparate COVID-19 treatment data into a KG to analyze drug–drug interactions and adverse effects (^[12]). Horned by the pandemic crisis, the team integrated drug databases (DrugBank), vocabularies (UMLS), and publications (CORD-19, literature) into a coherent resource.

Highlights:

• Mapping-based integration: They used the RDF Mapping Language (RML) to declaratively specify how each source’s records become RDF triples (^[9]). For example, a DrugBank entry for “hydroxychloroquine” and its drug-drug interactions is mapped to RDF properties linking drug URIs.

• Natural language processing: To capture information embedded in text, they applied NER to literature (COVID-19 studies) and mapped entities to schema classes (compounds, conditions) (^[12]). This enriched the KG with literature-derived relations (e.g. “drug A and B may interact”).

• Graph Schema: The KG schema included classes for Drug, DrugInteraction, AdverseEvent, and relationships like interactsWith, causesEffect (^[12]) (^[40]). This structured approach allowed linking, e.g. a drug interaction event to a set of adverse events.

• Analysis services: On top of the KG, they built analytic tools. One was a deductive system that, via inference rules, detected under-reported interactions based on known pharmacokinetics (^[41]). They also used ML models on the graph to predict new interactions (^[42]). In practice, they could then query “For patient on drug X with hypertension, what COVID drugs could cause risk?” by traversing the KG.

This project exemplifies several architecture points: ETL pipeline (RML for integration), combined use of structured and unstructured data (databases + text), and services (SPARQL endpoint, APIs, analysis engines) built on the KG (^[12]) (^[43]). The KG enabled them to overlay predicted toxicities and efficacy analysis. It is an example of how rapid KG assembly, even under crisis conditions, can address complex biomedical questions.

Comparative Analysis of Biomedical Knowledge Graphs

Several comprehensive reviews and databases have tabulated existing biomedical KGs (^[35]) (^[19]). Table 3 (below) summarizes a selection of major knowledge graphs relevant to drug discovery, drawing on recent literature (^[35]) (^[19]). The table highlights their scale (entities and triples), focus, and customization. For brevity, we focus on representative examples:

Table 3: Selected biomedical knowledge graphs linking compounds, targets, and related entities (sources as cited).

From this comparison, several trends emerge:

• Common Entities: Virtually all KGs include gene/protein, compound/drug, and disease entities (^[6]) (^[2]). Many also include pathways, anatomical terms, or phenotypes where relevant. The density of cross-links between these core nodes underpins their utility.

• Scale and Focus: Some KGs are broad (Clinical Knowledge Graph with >16M entities ), while others (PharmKG) are deliberately narrow (focusing on high-quality drug–gene–disease triplets). The size depends on use case: repurposing (Hetionet) requires wide connectivity; drug discovery (PharmKG) optimizes for precision. Benchmarks like OpenBioLink aim for comprehensiveness to test algorithms (^[46]).

• Data Sources and Curation: KGs vary in curate vs auto-extract balance. Hetionet and PharmKG begin with curated data (DisGeNET, PharmGKB, SIDER); PrimeKG and Knowledge4COVID combine curated and pipeline-extracted data (literature). Ontology overlap is common (e.g., many use MeSH or DOID for disease terms).

• Features vs Schema: PharmKG uniquely provides numeric feature vectors per node (molecular fingerprints, gene expression) to facilitate graph ML (^[18]). Hetionet/DRKG provide relations but not features. Most graphs do not embed raw assay values (an area for future work).

• Accessibility: All referenced KGs make their data available (either via download or API) (^[35]) (^[46]). Open APIs or SPARQL endpoints (like Open PHACTS, SPOKE, Pharos) are common to allow queries. Visualization and workflow integration (KNIME nodes, Python libraries) are often provided to bring graph data into analysis pipelines (^[20]).

In summary, knowledge graphs in drug discovery share a roughly consistent architecture: they aggregate multiple sources into unified RDF graphs with a defined schema, and make it available for query and analysis. Differences lie mainly in scope (which biomedical domains they emphasize) and in enriching data for machine learning (embeddings, features). Our architecture discussion applies broadly: all the above graphs would use some ETL pipelines for ingestion and rely on semantic normalization of entities/relations.

Integration Pipelines and Tools

In building a discovery KG, practitioners use a variety of software tools and frameworks. Key categories include:

• RDF Triple Stores and Graph Databases: Both semantic (RDF) and property graph databases are used. RDF stores like GraphDB, Blazegraph, Apache Jena, and Virtuoso support SPARQL queries and OWL reasoning. Property graph engines (e.g. Neo4j, TigerGraph, AWS Neptune) use languages like Cypher or Gremlin. The choice often depends on team expertise and needs (OWL reasoning, RDF stack vs high-performance pattern matching). For example, Hetionet was modeled in Neo4j (Cypher) (^[35]), while PrimeKG and Knowledge4COVID use RDF stores for semantic queries (^[47]) (^[12]).

• ETL and Mapping Platforms: Tools like Ontop (for RDB-to-RDF), TripleCandy, RDFLib (Python library), and RMLMapper implement the mapping step. Workflow systems (KNIME, Apache NiFi, Pentaho) often orchestrate ETL logic. In Open PHACTS, custom scripts transformed and cleaned each source for RDF loading (^[20]). Knowledge4COVID-19 relied on declarative RML mappings (^[9]).

• Identifier and Ontology Services: Services like UniChem for compounds, BioPortal or EMBL’s Ontology Lookup Service (OLS) help map names to ontology terms. Ontology alignment tools (e.g. OntoRefine, PROMPT) can merge similar classes. Data curation frameworks (e.g. OpenRefine with RDF extensions) allow semi-automated linking of entities between datasets.

• APIs and Microservices: Many architectures expose graph queries via RESTful APIs or microservices. For example, the Illuminating the Druggable Genome’s Pharos portal uses a GraphQL API over its integrated TCRD data (^[48]), while others use Swagger-described REST services (Open PHACTS) or custom endpoints (Knowledge4COVID (^[49])). Containerization (Docker/Kubernetes) is common for deploying services, enabling horizontal scaling for heavy KG queries.

• Graph Analytics and ML Libraries: On top of the KG, libraries such as DGL (Deep Graph Library), PyTorch Geometric, or NetworkX are used to run graph algorithms (clustering, embeddings, link prediction (^[13])). For example, DRKG provided precomputed graph embeddings using DGL (^[35]). Platforms integrating KG queries with ML (e.g., embedding extraction) are emerging; some projects (PertKGE (^[13])) build specialized KGs to feed into embedding models.

• Semantic Reasoners: When ontologies are rich, OWL reasoners (Pellet, Hermit) may infer implicit triples. The BAO team demonstrated ontology-based inference on assay queries (^[6]). GraphDB and Fuseki have built-in OWL support.

• Visualization and User Tools: Tools like Cytoscape or built-in graph explorers may be used for manual exploration. In practice, many efforts focus on programmatic access (via Jupyter notebooks, KNIME) rather than ad-hoc UI.

Figure 1 (below) illustrates a notional architecture: multiple source databases feed into an integration layer (ETL/RML), which populates a graph database. A semantic layer ensures consistent vocabularies. On top, query APIs or workflow nodes enable researchers to link compounds, assays, and targets in analysis pipelines.

Figure 1 (conceptual): Layered architecture for a biotech knowledge graph. Data sources (left) – ranging from chemical registries to pathway DBs – are ingested via ETL or mapping into the Graph Store (center). Ontologies and identifier services (middle) harmonize terms. Upward, user-facing APIs and analytic tools (right) allow scientists to query and analyze relationships (e.g. compound→target→outcome paths).

Analytical Capabilities

With a KG in place, various analyses become possible:

• Complex queries: e.g. Find all compounds that were active (IC50 < 100 nM) in any assay targeting proteins in the PI3K/Akt pathway and have no recorded adverse cardiac events. This involves joins across Compound–assay–target–pathway–adverseEvent in the graph. Such queries are expressed in SPARQL (with optional reasoning via transitive properties).

• Link Prediction: Graph embeddings (node2vec, TransE, DistMult) can predict novel edges. Many efforts use KG completion to suggest drug–target links or drug–disease associations (^[13]) (^[50]). For instance, the PertKGE model used graph embeddings on a multi-layer biological KG to predict compound–target interactions from transcriptomic perturbations (^[13]), significantly aiding “cold-start” cases for new compounds.

• Path Ranking and Explainability: Inferences often hinge on multi-hop paths (compound–gene–disease). Methods like metapath counting, or path-based machine learning (as in Neo4j Graph Data Science), can rank and explain predictions via graph patterns. The GNBR project (Pulido et al. 2019) and SemaTyP used paths (either formally defined or learned) through their KGs to repurpose drugs (^[51]) (^[52]).

• Integration with ML/DL: As noted, KG embeddings feed standard ML models. Visualizations or network analysis (centrality, community detection) on the KG can highlight hubs (pleiotropic targets, polypharmacologic compounds).

• Reasoning over Ontologies: The presence of ontologies (GO, BAO) enables deductive inferences. For example, if an assay has format “GPCR binding assay” (a BAO term), a reasoner can infer it is a subset of “binding assay,” broadening queries.

• Statistical Analysis: Some KGs include quantitative data (e.g. potency, gene expression profiles). Statistical summaries (e.g. enrichment of pathways among targets of a compound) can be computed by aggregating graph data.

Overall, the KG does not just store data—it enables knowledge discovery. For instance, in the Knowledge4COVID-19 case, the authors discovered two novel COVID drug interactions that weren’t documented originally, by combining KG deduction and ML prediction (^[42]). Similarly, the Chem2Bio2RDF team reported finding new adverse drug–pathway associations via semantic query (^[2]).

Discussion: Challenges and Future Directions

The foregoing sections outline how knowledge graphs can link compounds, assays, targets, and outcomes. However, realizing this vision faces challenges and evolving trends:

• Data Quality and Licensing: As noted in Open PHACTS (^[53]), heterogeneous data have inconsistent formats and identifiers. Cleaning and reconciling these (normalizing false synonyms, missing values) is time-consuming. Licensing can restrict reuse (e.g. clinical data, proprietary assays). Solutions include careful provenance tracking and favoring open datasets.

• Scalability: Graph databases may struggle with extremely large graphs or complex analytics. Some biomedical KGs now have >200M triples (^[54]). Scaling SPARQL or Cypher to millions of nodes and supporting inference remains nontrivial. Hybrid architectures (sharding, cloud graph services) are emerging. From Ontotext’s perspective, modular patterns like incremental replication can alleviate loads (^[8]).

• Schema Evolution: Biomedical knowledge evolves (new targets, assays, definitions). A KG must adapt: adding new node types or relations without breaking existing queries. One strategy is using a meta-model (e.g. Wikidata-like model) that is extensible. Another is versioning using named graphs and maintaining backward compatibility columns (old vs new schema IRIs).

• Integrating Unstructured Data: Much knowledge still lives in texts. While projects like FORUM (^[11]) and Knowledge4COVID (^[12]) show progress, automated pipelines (NLP+KG) are not lossless. Entity linking often produces noise. Future systems may leverage large language models to extract semantics more accurately and align them with KG nodes.

• Explainability: In drug discovery, transparency is important. If a KG-based model suggests a compound-target link, scientists will ask “why”. Path-based explanations (showing the chain of relationships) help, but require that the underlying data is interpretable. Ontology alignment and consistent semantics assist explainability. Combining symbolic KG reasoning with machine learning remains an active research area.

• Privacy and Security: For KGs involving clinical outcomes or patient data, privacy is critical. Architectures may deploy data virtualization or federated query (so sensitive data never leaves institutional control). Secure authentication and encryption must be integrated into query services. The heterogeneous nature of discovery data (some proprietary, some public) further complicates governance.

• FAIR and Open Data: The momentum of FAIR principles means future KGs will emphasize findability and reusability. Designs may increasingly use linked FAIR Data Principles: globally unique IDs, rich metadata, standardized vocabularies (^[22]) (^[55]). Initiatives like GO FAIR advocate Knowledge Graph-based FAIR implementation. We expect KG projects to provide open SPARQL endpoints or data dumps, with clear licensing (e.g. PrimeKG provides scripts to update from new releases (^[56])).

• AI Integration: Looking ahead, knowledge graphs will feed (and be fed by) AI models. Graph neural networks and embedding models will play a bigger role (as in PertKGE (^[13])). Conversely, pretrained LLMs might be tuned using KG triples to improve biomedical reasoning. A unified architecture could allow iterative refinement: ML-generated hypotheses can be checked/embedded back into the KG.

• Semantic Layer to Data Lake: The trend of combining data lakes with semantic layers (as in Figure 1) will grow. Tools that can dynamically expose parts of a data lake as a graph (data virtualization) will mature, blurring ETL roles. Companies like Modak propose multi-layer pipelines that start from raw lake and progressively add ontology layers (^[57]) (^[58]).

• Benchmarks and Standards: To gauge progress, KG benchmarks are emerging (OpenBioLink, HEtionet for repurposing, etc. (^[46])). Standard query workloads and metric suites will drive best practices. Also, ontology centralization efforts (e.g. UMLS, OBO Foundry) will improve consistency in target vocabulary.

Conclusion

Linking compounds, assays, targets, and outcomes into an integrated graph offers a transformative approach for drug discovery and biotech research. By adopting knowledge graph architecture patterns—ETL/ELT pipelines, stable ontology-driven schemas, and modern data integration techniques—researchers can unify fragmented data into a cohesive semantic network. As demonstrated by platforms like Open PHACTS, Chem2Bio2RDF, FORUM, and Knowledge4COVID-19, such graphs enable queries and inferences that would otherwise require exhaustive manual cross-referencing (^[3]) (^[11]). They have been used to identify novel drug-target hypotheses, understand polypharmacology, and predict adverse outcomes.

The journey to robust discovery KGs involves careful engineering: mapping identifiers, choosing appropriate integration patterns (^[7]) (^[8]), and managing trade-offs between up-to-dateness and complexity. It also requires community collaboration on ontologies (like BAO for assays, GO for targets) and data sharing standards. As data volumes continue to grow, these knowledge-graph-driven architectures—backed by semantic web best practices—are poised to become essential infrastructure in biopharma R&D. Future advances (cloud-native graph services, AI-driven knowledge extraction, FAIR-at-scale implementations) will further amplify their impact. In embracing these systems, the biotech community can unlock hidden insights across compounds, assays, targets, and outcomes, accelerating path-to-discovery in a more connected data landscape.