Drug discovery AI depends on research data that can be found, understood, connected, traced to its source, and used under the right permissions. Building that foundation is not a matter of choosing one platform or copying a regulatory submission format. It means deciding how your organization will describe, standardize, connect, and govern its data so the right research questions can be answered reliably.
What does a unified data foundation need to do?
A unified foundation makes data from different systems usable together without erasing where it came from or what it means. In practice, it is a managed set of metadata, identifiers, formats, integration patterns, and governance rules—not necessarily one central database.
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The U.S. Food and Drug Administration (FDA) defines data standards as rules for structuring, defining, formatting, or exchanging data between systems. Consistent standards can make information more predictable for scientific tools and let FDA scientists explore questions by combining data from multiple studies. The FDA’s Center for Drug Evaluation and Research says it handles more than 300,000 submissions a year, amounting to millions of pieces of data. That figure describes the agency’s regulatory workload, not the expected scale or performance of a drug-discovery AI project.
For discovery work, the goal is more specific: enable researchers and computational tools to locate appropriate datasets, interpret their context and limitations, and combine them for a defined purpose while respecting access restrictions.
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Start with the research questions and constraints
Do not begin by selecting a database, graph platform, or cloud service. Begin with the decisions researchers need to make. For each priority question, identify which data would help answer it, where those data are held, who can use them, and what quality or timing constraints apply.
Questions might require linking experimental results to sample context, connecting observations from different studies, or interpreting a result alongside relevant product information. The necessary data domains vary by organization and question; no fixed list is exhaustive.
- Define the question: State what a researcher or model should be able to find, compare, or infer.
- Map the data: Record the source systems, owners, formats, identifiers, and known limitations for the relevant datasets.
- Check permissions: Establish whether access is open, restricted to approved users, limited to a purpose, or otherwise controlled.
- Set a useful boundary: Choose a pilot question narrow enough to validate end-to-end, but meaningful enough to expose real integration problems.
This mapping turns “unify the data” into testable requirements. It also helps avoid integrating information that is not needed for the intended use.
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Make data discoverable and interpretable
A shared catalog or equivalent discovery layer should help a user answer basic questions before attempting analysis: What dataset exists? Who produced it? How was it generated? What does each field or measurement mean? What are the known limitations, version, and permitted uses?
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The NIH Common Fund Data Ecosystem (CFDE) is a public example of a portal intended to help researchers search across dispersed Common Fund datasets from one access point. NIH describes CFDE as integrating data, resources, and knowledge across programs and supporting FAIR-oriented cross-dataset discovery. FAIR means findable, accessible, interoperable, and reusable; “accessible” in this context does not mean that every dataset must be publicly downloadable. A dataset can be discoverable while access remains controlled.
For an internal catalog, prioritize metadata that helps people judge relevance and suitability, not merely locate a file. Include source, collection or generation context, definitions, units where applicable, transformations, version, ownership, quality notes, and access conditions. Keep catalog records connected to authoritative data locations rather than treating a metadata listing as the data itself.
Standardize where it serves a defined use
Standards can align definitions, formats, identifiers, and exchange rules across systems. They are most valuable when they support a concrete task, such as comparing studies or reliably identifying a medicinal product. Applying a single schema indiscriminately can be costly and can discard source-specific detail; retaining every source format without mappings makes cross-dataset work difficult.
The FDA distinguishes required standards from standards that are not required and directs users to its catalog for supported and required standards and future timelines. That means an FDA standard should not automatically be treated as mandatory for every discovery dataset. Select standards according to the use case, relevant obligations, and intended exchange.
For medicinal-product identification and related regulatory information, IDMP is a relevant standards family. ISO/TS 21405:2026 describes an ontology framework intended to support semantic interoperability for medicinal-product identification using IDMP standards and FAIR principles. The specification explains a framework for concepts and relationships; it does not mandate a particular ontology implementation tool.
Connect systems while preserving meaning and source
There is no single architecture mandated by the sources described here. A foundation can combine centralized storage, federated access, shared models, mappings, APIs, batch pipelines, or graph representations. Choose based on the research questions, data characteristics, access constraints, freshness needs, and operational capacity.
NSF’s Open Knowledge Network offers an example of federated knowledge graphs connected through a shared technical fabric. NSF’s September 25, 2026 announcement describes 43 interconnected knowledge graphs containing tens of billions of connected facts, and cross-graph questions that can reach beyond any one graph. The initiative reports $26.7 million and 18 research teams in its initial prototype effort, launched in 2023, as well as participation from more than 12 federal agencies and over 90 cross-sector partnerships. These are figures about NSF’s infrastructure and program, not pharmaceutical discovery datasets or a forecast for a particular project.
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That example demonstrates one way to connect independently managed resources; it is not evidence that every drug-discovery environment needs a knowledge graph. NSF Assistant Director for Technology, Innovation and Partnerships Erwin Gianchandani said, “What launches today is public infrastructure that agencies, researchers, and the public can use to answer questions that cross the boundaries between fields,” in the September 25, 2026 announcement.
When integrating data, keep mappings and links inspectable. A result should remain traceable to its source record and the transformations that made it usable. Otherwise, a unified view can make unlike measurements appear equivalent or obscure why a value differs across systems.
Choose architecture by tradeoff, not fashion
The options below are design dimensions, not a ranking or a prescribed stack. Many practical systems combine choices from multiple rows.
| Decision | Favors | Tradeoff to evaluate |
|---|---|---|
| Centralized storage or federated access | Centralized storage can simplify operational control and consistent processing. Federation can preserve distributed ownership and reduce unnecessary duplication. | Centralization can require copying data and reconciling ownership. Federation can make cross-system queries, performance, and access coordination more complex. |
| Shared schema or mappings between source schemas | A shared schema can simplify common analysis. Mappings can retain source structures while translating them for selected uses. | A shared schema can impose migration and maintenance work. Mappings require ongoing translation rules and clear handling of fields that do not align. |
| Relational or tabular model, or knowledge graph | Tables and relational models suit well-structured records and many standard processing tasks. Graphs can represent entities and relationships across sources explicitly. | A graph is not automatically better for every workload; consider query needs, complexity, existing skills, and maintenance. Tabular models may require additional work to express complex cross-source relationships. |
| Batch pipelines or event/API-based integration | Batch pipelines support repeatable scheduled ingestion. Event or API integration can make updates available sooner. | Batch processing can leave data less current between runs. More immediate integration adds operational and monitoring complexity. |
| Open or shared infrastructure, or commercial managed services | Open and shared infrastructure can offer control and portability. Managed services can reduce some operational burden. | Portability and control must be balanced against the work of operating infrastructure; managed operations can create provider dependency. Evaluate both against actual requirements. |
Preserve provenance and govern permitted use
AI-ready data needs more than a common format. Users should be able to determine where data came from, the context in which it was generated, which transformations were applied, which version was used, and what uses are permitted. Record those details in a way that travels with, or remains reliably linked to, downstream datasets and model inputs.
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Make governance operational rather than leaving it in policy documents alone. For example, a catalog entry can expose access conditions, while an integration service enforces them. Document who approves changes to shared definitions, how corrections propagate, and how users can distinguish missing data from data they are not authorized to see.
Keep regulatory submission standards in their proper scope
Regulatory interoperability matters, but it is not the same as a research data architecture. The FDA’s data standards program covers defined standards and requirements for regulatory submissions, including study data and product information. Its December 2023 final guidance, Data Standards for Drug and Biological Product Submissions Containing Real-World Data, is scoped to submissions containing real-world data.
Use these materials to understand relevant submission requirements and prepare data for applicable regulatory exchanges. Do not assume they prescribe how to organize all discovery data, including preclinical, assay, imaging, omics, or literature information. Check the applicable FDA standards catalog and guidance for the particular submission context rather than generalizing a requirement to unrelated data.
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Test one real research question from discovery through authorized use and analysis. The point is to learn whether the foundation works for people and tools—not merely whether files have been copied or records can be indexed.
- Register the inputs: Add the selected datasets to the catalog with owners, source context, definitions, limitations, versions, and access conditions.
- Apply only necessary standards: Map relevant fields, units, and identifiers to shared definitions, retaining links to original records and documenting unresolved differences.
- Connect the data: Implement the simplest integration pattern that meets the question’s needs, whether that is a repeatable pipeline, a federated query, or another suitable approach.
- Enforce permissions: Verify that users and services can retrieve only data allowed for their role and purpose.
- Trace an output backward: Confirm that a result can be traced to its input records, transformations, and versions.
- Ask researchers to assess usefulness: Check whether they can locate relevant data, understand its context, and recognize limitations without relying on undocumented knowledge.
Use the pilot to expose gaps in metadata, ambiguous definitions, access rules, or maintenance ownership before expanding. A foundation is sustainable only if responsibility for shared definitions, mappings, connectors, and provenance records is clear.
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