FAIR by Design at DeltaSoft
Making chemistry R&D data findable, accessible, interoperable, and reusable
The FAIR data principles—Findable, Accessible, Interoperable, and Reusable—are a practical framework for making scientific data and metadata usable by both humans and machines at scale. FAIR does not mean “open by default”; data can remain permissioned. And FAIR is not a one-time checklist—teams usually adopt it incrementally, starting with the workflows and datasets that matter most.
At DeltaSoft, we built our R&D informatics platform with FAIR outcomes in mind. Our data model and governance features are designed, so chemistry data stays durable, linkable, and AI-ready over time—without compromising security or control.
How DeltaSoft Supports The FAIR Principles
Findable
Findability starts with persistent identifiers and rich, structured metadata, so key scientific objects can be unambiguously referenced, searched, and linked across teams and systems.
DeltaSoft supports findability by:
- Assigning unique IDs to chemical structures, batches of compounds, and chemical reactions.
- Linking those IDs to structured metadata so entities can be searched by scientific context (not just filenames).
- Maintaining consistent references across projects so the same structure/batch/reaction is not duplicated under multiple names.
Accessible
Accessible means authorized users and systems can retrieve data and metadata by identifier using standardized protocols, with authentication and authorization where appropriate.
DeltaSoft supports accessibility by:
- Providing programmatic access via REST APIs documented through OpenAPI.
- Enforcing comprehensive permissions so the right users can access the right datasets while maintaining governance.
- Keeping metadata durable and retrievable even as underlying artifacts evolve or are archived, so discovery and interpretation remain possible over time.
Interoperable
Interoperability is about consistent meaning across tools. It relies on standards-aware data structures and shared vocabularies/ontologies so data can be integrated and interpreted without brittle, one-off translations.
DeltaSoft supports interoperability by:
- Supporting industry-standard APIs and structured representations that preserve meaning, not just raw files.
- Using controlled vocabularies and a normalized data model to keep key concepts consistent across teams and workflows.
- Configuring ChemCart to match your organization’s data standards and terminology, ensuring consistent use of reaction roles, conditions, assay types, and units of measure across teams and projects.
- Enabling export and integration patterns that maintain semantic context across systems (e.g., mappings, canonical fields).
Reusable
Reusable data carries enough context and provenance to be correctly interpreted and trusted in the future. That typically means clear provenance, versioning, and governance so teams can reproduce analyses and build confidently on past work.
DeltaSoft supports reusability by:
- Maintaining a comprehensive audit trail so users can see who changed what, when, and why.
- Providing robust versioning so historical states of records are preserved for reproducibility and longitudinal analysis.
- Combining provenance with permissions so reuse does not require sacrificing governance.
FAIRification in Practice (a pragmatic path)
FAIR adoption is often most successful when it is incremental. A practical way teams use
DeltaSoft to move toward FAIR outcomes is:
- Register structures, batches, and reactions with unique IDs so everyone references the same entities.
- Standardize metadata capture with templates/required fields and controlled vocabularies so records stay consistent.
- Apply shared ontologies/standards so integration across ELN/LIMS/analytics tools is reliable and machine-actionable.
- Rely on provenance (audit trail + versioning) so downstream users—and machines—can trust what happened.
Chemistry-specific FAIR: structures and reactions
FAIR can feel abstract until you apply it to the chemistry objects that actually drive R&D: structures, samples/batches, and reactions. Community guidance on FAIR chemistry data consistently emphasizes that internal IDs are necessary, but chemistry data becomes truly FAIR when the structures and reactions behind those IDs are also represented in machine-readable, standards-aware ways and accompanied by clear provenance.
- Store at least one machine-readable structure representation alongside the identifier (e.g., SMILES and/or InChI), plus human-readable names/synonyms—avoid relying only on a name or a hashed key.
- Make structure exchange predictable: support common interchange formats for sharing and downstream reuse (e.g., CSV/TSV templates or SDF with clearly labeled fields).
- Treat normalization as provenance: track the original “as-drawn” representation vs any normalized/canonical form (salts, stereochemistry, tautomers, charges), and record the rules/version used.
- Give reactions the same FAIR treatment as structures: capture reaction participants/roles and consider open reaction identifiers (e.g., Reaction InChI / RInChI) to make transformations linkable across tools.
- Use shared vocabularies/ontologies for reaction roles, conditions, properties, units, and terminology so the meaning survives integration across ELN/LIMS/analytics systems.
- Export compounds and reactions, along with their classifications and associated data, into a single file. ChemCart supports Excel, CSV, SDFile, RDFile, and XDFile formats for easy sharing and analysis.
Concrete metadata examples
FAIR becomes tangible when you can point to the specific fields and relationships captured at registration time.
- Every newly registered batch is automatically assigned standard identifiers and key compound properties, reducing manual entry and minimizing errors. ChemCart also calculates and records details such as molecular weight, molecular formula, compound name, registration date, and registrant information. Additional fields—including salt form, stereochemistry, project, therapeutic target, and library—can be added as needed.
- Newly registered reactions include all relevant compound information, along with reagent and product data such as weights, masses, moles, and volumes. ChemCart automatically calculates missing values, including actual mass and percent yield, helping users save time and improve data accuracy.
A simple FAIR reality check to understand how you are doing
A quick internal checklist to pressure-test FAIR alignment:
- Can we find the dataset by scientific intent (not just filenames)—and does it have a stable ID?
- Can authorized users and systems access it via standard protocols (UI + API) with governed permissions?
- Can we integrate it with other systems without rewriting everything (shared vocabularies/ontologies/standards)?
- Can we reuse it confidently because provenance, quality context, and usage rules are clear?
If your answers are mostly “yes” (instead of “it depends”), your FAIR foundations are in good shape—and your data is far more ready for automation and AI.
Want a FAIR assessment?
If you’d like, contact us and we’ll review how your current data model, metadata practices, and governance map to the FAIR principles—and suggest pragmatic next steps to improve findability, interoperability, and reuse.
Further reading
- GO FAIR: FAIRification Process — https://www.go-fair.org/fair-principles/fairification-process/
- Chemistry GO FAIR Implementation Network (ChIN) Manifesto — https://www.go-fair.org/wp-content/uploads/2019/02/ChIN-Manifesto-final-20180905.pdf
- Coles SJ, Frey JG, Willighagen EL, Chalk SJ. Taking FAIR on the ChIN: The Chemistry Implementation Network. Data Intelligence. 2020;2(1–2):131–138. doi:10.1162/dint_a_00035
- Schymanski EL, Bolton EE. FAIR chemical structures in the Journal of Cheminformatics. Journal of Cheminformatics. 2021;13:50. doi:10.1186/s13321-021-00520-4
Ready to Improve Your Research Operations?
Schedule a personalized demo to see how DeltaSoft can help streamline workflows, improve data management, and support better scientific outcomes.
