Methods¶
This page summarizes the implemented methodology used by the pipeline. Detailed API signatures are documented under Reference.
Data Ingestion and Standardization¶
Loaders normalize split inputs into canonical
train,valid,testframes.Tabular data supports CSV/TSV/Parquet with configurable column mapping.
DTI loading additionally normalizes sequence and target-ID columns.
Optional SMILES cleaning can canonicalize structures, deduplicate, and annotate quality flags.
Similarity Computation¶
BenchAudit computes multiple complementary similarities:
Molecular similarity using Morgan fingerprints (configurable radius and bit length).
Scaffold similarity using generic Murcko scaffold fingerprints.
String-level similarity on SMILES using normalized Levenshtein similarity.
Nearest-neighbor similarity summaries are reported for validation/test against training and train+valid references.
Conflict and Cliff Detection¶
Classification conflicts: identical cleaned SMILES with differing labels.
Regression conflicts: identical cleaned SMILES with label deltas beyond a 3-sigma threshold.
Activity cliffs: highly similar molecule pairs with divergent labels under the same task rules.
DTI-Specific Diagnostics¶
DTI mode extends molecule-level auditing with target-level checks:
target sequence overlap and duplication statistics across splits
cross-split ligand-target pair reuse checks
nearest-neighbor sequence alignment diagnostics via EMBOSS
stretcheroptional structure-level leakage diagnostics when Foldseek alignments are provided
Baseline Benchmarking¶
With --benchmark, BenchAudit trains lightweight baseline models and writes
performance.json. This is intended as context for dataset difficulty and sanity checks,
not as a deployment pipeline.
Verification¶
The repository includes unit tests for loaders, pipeline orchestration, baselines, and result writing. Build docs locally with:
sphinx-build -W --keep-going -b html docs/source docs/_build/html