Datasets:

SupraBench
/

SupraDB-GEOM

license: other
pretty_name: SupraDB-GEOM
tags:
  - chemistry
  - supramolecular-chemistry
  - rdkit
  - cb7
configs:
  - config_name: default
    data_files:
      - split: train
        path: guests.csv

SupraDB-GEOM

What it is

SupraBench/SupraDB-GEOM is the Phase 0 guest-pool dataset for the SupraBench compute pipeline. It contains the canonical guest identity table used as the source of truth for downstream ligand-only scoring, docking-derived pose features, cavity scores, and dashboard joins.

Rows are built from GEOM source SMILES, optional QM9 source SMILES, and optional experimental CB[7] labeled guests. Each row starts from an input SMILES string, is parsed with RDKit, desalted by keeping the largest fragment, converted to canonical SMILES, and assigned an RDKit-derived InChIKey. Rows are deduplicated by derived InChIKey with priority labeled > geom_drugs > qm9.

Schema

Column	Dtype	Units	Meaning
`inchikey`	string	none	RDKit InChIKey derived from the canonical largest-fragment SMILES; primary join key across all SupraDB datasets.
`name`	string	none	Guest name from the source CSV when present, otherwise the first 14 characters of `inchikey`.
`smiles`	string	none	RDKit canonical SMILES after largest-fragment desalting.
`logka`	float/string nullable	log10 association constant	Optional experimental CB[7] binding label from the source CSV; empty for unlabeled guests.
`source`	string	none	Winning source after priority deduplication: `labeled`, `geom_drugs`, or `qm9`.
`n_heavy`	int64	atoms	Heavy-atom count of the canonical largest-fragment molecule used to derive `inchikey`.

Join key

inchikey is the sole join key for SupraDB-GEOM, SupraDB-LigandScore, SupraDB-PoseFeat, and SupraDB-CavityScore. It is generated once here from canonical largest-fragment SMILES so downstream compute, cache entries, 2D/3D structure lookup, and dashboard records all use the same identifier.

Provenance

Pipeline position: Phase 0 pool builder, before CRC ligand scoring and GLIDE docking.
GEOM source CSV: data/geom_drugs_smiles.csv.
QM9 source CSV: data/qm9_smiles.csv.
Labeled merge: 63 labeled rows were merged from the labeled CSV.
Canonicalization: RDKit MolFromSmiles -> largest fragment by heavy atom count -> MolToSmiles(canonical=True) -> MolToInchiKey.
Row count: 430904.
Output rows by source:
labeled: 63 rows
geom_drugs: 301523 rows
qm9: 129318 rows
Downstream compute context: CRC; GLIDE 2025u2 / aISS fallback applies to later docked feature datasets, not this pool table.
Pose collapse rule: not applicable for this dataset; later pose datasets keep the highest-Boltzmann-weight pose by default.
Exact command: /users/tma2/workspace/SupraDashboard/.venv/bin/python engineering/src/pool_builder.py --input data/geom_drugs_smiles.csv --qm9 data/qm9_smiles.csv --labeled engineering/data/guests.csv --out data/pool_full --workers 8.