Biological Data Infrastructure
Life sciences AI inherits the strengths and weaknesses of biological archives. Protein structures, sequences, small molecules, perturbation screens, and imaging datasets carry different biases, missingness patterns, and validation traditions.
- Map the main data classes used by life sciences AI systems.
- Identify when a dataset is machine-readable but not AI-ready.
- Explain why metadata, assay provenance, and negative examples matter.
Better models do not rescue poorly specified biological data. AI-ready data require provenance, assay context, versioning, licensing, and negative controls. The most useful model card is often a dataset card.
Introduction
The Protein Data Bank has served as a global archive for experimentally determined macromolecular structures since 1971 (wwPDB, 2026). RCSB PDB now also presents computed structure models beside experimental structures, which forces users to distinguish measurement from prediction (RCSB PDB, 2026). For biomedical AI, the same distinction applies across sequence archives, chemical databases, single-cell atlases, and image repositories.
Demonstrated
Demonstrated capability includes training and evaluating models on curated public resources. ChEMBL provides curated bioactivity data for drug-like molecules (Zdrazil et al., 2024). PubChem provides chemical substance, compound, and bioassay records through NIH infrastructure (Kim et al., 2023). AlphaFold DB and the ESM Metagenomic Atlas show how predicted structures became research resources at database scale (AlphaFold Protein Structure Database, 2026; ESM Metagenomic Atlas, 2026).
| Evidence Anchor | What It Supports | Practical Constraint |
|---|---|---|
| PDB | Experimentally determined structure archive | Coverage follows what structural biology could measure |
| ChEMBL and PubChem | Chemical structure and bioactivity resources | Assay context and curation level differ across entries |
| Bridge2AI | AI-ready data standards as a program objective | Ethical sourcing, metadata, and fairness are part of data quality |
Theoretical
Theoretical capability includes cross-database models that learn from raw sequence, structure, chemical, image, and text records without manual harmonization. This remains theoretical in many workflows because identifiers, assay conditions, version histories, and licensing terms often fail to align cleanly.
Beyond Current Capabilities
Beyond current capabilities includes biological datasets that fully encode the causal context of an experiment. No public archive contains all cell states, reagent histories, operator choices, instrument behavior, and environmental variables needed to remove experimental ambiguity.
Practice Notes
- Record data version, download date, accession source, and filtering logic.
- Separate experimental structures from computed structure models in tables and figures.
- Keep negative, failed, and inconclusive experiments when training prioritization systems.
- Audit license terms before mixing public, consortium, and commercial data.