Dataset Construction Checklist

A dataset construction checklist turns a modeling idea into an auditable dataset. It should be written before interpreting metrics, because most evaluation failures start as data-definition failures.

A compact construction pipeline is:

where is the source record set, is the preprocessing contract, and is the split rule.

Checklist

Molecular Modeling Checks

  • Molecules: standardization, tautomer/protonation/stereo policy, scaffold grouping.
  • Proteins: sequence identity, family grouping, structure source, confidence, missing regions.
  • Protein-ligand complexes: receptor preparation, ligand preparation, pocket definition, pose source.
  • Assays: target context, endpoint, threshold, units, censoring, and harmonization.
  • Structure-based tasks: template leakage, ligand-defined pocket leakage, and deployment-available context.

Public Writing Boundary

For a public blog, describe dataset construction as a general method unless the dataset is public and the metadata is verified. Do not publish private dataset names, collaborator details, internal paths, unpublished results, or non-public task identifiers.