ChemTrace: Relation-Typed Provenance for Molecular Pretraining-Corpus Audits

Molecular models are often pretrained on large chemical corpora and evaluated on reused public benchmarks. Potential overlap between benchmark molecules and pretraining records complicates the interpretation of model evaluation: exact matching misses standardized-parent, stereochemical, and tautomeric relations, whereas generic similarity cannot establish which candidate-corpus record supports a declared molecular relation. Our goal is to recover attributable molecular exposure—an auditable typed relation between a benchmark molecule and an inspectable candidate-corpus record—without requiring model access. We introduce CHEMTRACE, a provenance framework that indexes canonical identity, standardized parent, stereochemistry-stripped connectivity, and canonical tautomer form. It emits provenance certificates, versioned JSON records that bind fixed corpus, split, and intervention specifications to relation-typed retrieval evidence. Independently defined ChEMBL36 registry relations and curated Tautobase tautomer pairs yield 0.913–0.992 primary-relation recall across the three non-exact relations. CHEMTRACE recovers every changed non-exact exposure in the controlled audit, emits no certificate on 500 deliberately separated registry negatives, and remains silent across a 3,948-pair hard-neighborhood bank explicitly conditioned to be typed-relation-negative. Across six MoleculeNet datasets and 13 same-protocol audit routes, CHEMTRACE is the only evaluated method combining complete changed-only recall, a 0.0036 ± 0.0032 background hit rate, and zero decoy hits. A two-stage scaffold/seed bootstrap finds no consistently positive downstream inflation across 51 primary-estimator cells. Together, CHEMTRACE gives benchmark auditors a reproducible, record-level way to identify source-linked molecular exposure without treating chemical-neighborhood similarity as provenance; downstream model effects remain a separate empirical question.