---
license: other
pretty_name: SupraDB-CavityScore
tags:
  - chemistry
  - supramolecular-chemistry
  - cb7
  - glide
  - crc
configs:
  - config_name: default
    data_files:
      - split: train
        path: features.csv
---

# SupraDB-CavityScore

## What it is

`SupraBench/SupraDB-CavityScore` is a SupraBench feature dataset generated from the
SupraEngineering compute pipeline. Pipeline position: Phase 1 docked-subset GLIDE feature computation by compute_all_features; Phase 2 publish split.

The table is designed to join with the Phase 0 `SupraDB-GEOM` identity table and
the other feature datasets through `inchikey`.

## Schema

| Column | Dtype | Units | Meaning |
|---|---|---|---|
| `inchikey` | string | none | Primary InChIKey join key shared across SupraDB-GEOM, LigandScore, CavityScore, and PoseFeat. |
| `name` | string | none | Guest name from the canonical SupraDB-GEOM identity table. |
| `smiles` | string | none | Canonical largest-fragment SMILES from the canonical SupraDB-GEOM identity table. |
| `source` | string | none | Winning source from the canonical SupraDB-GEOM identity table after priority deduplication. |
| `docked` | bool | none | Boolean flag emitted only with --attempted for CavityScore and PoseFeat; false rows were submitted for docking but absent from the docked pickle and have empty feature values. |
| `batch` | string | none | Optional publish batch identifier emitted for CavityScore and PoseFeat when --batch is provided; every row from the publish run receives the supplied value. |
| `S_occupancy` | float32 | unitless score | Cavity occupancy score from the hydrophobic occupied volume of the selected docked pose. |
| `S_portal` | float32 | unitless score | Portal compatibility score from cation-portal distance, charge accessibility, hydrogen bonding, and orientation. |
| `S_accessibility` | float32 | unitless score | Charge accessibility score from solvent-accessible positive atoms in the selected docked pose. |
| `S_orientation` | float32 | unitless score | Orientation score measuring whether the positive center points from the cavity center toward the near portal. |

When `--attempted` is used for CavityScore or PoseFeat, `docked` marks whether a submitted guest is present in the docked pickle. Rows with `docked=False` were attempted but missing from the pickle, so their feature columns are empty/NaN. This flag is not emitted without `--attempted` and is never emitted for LigandScore.


When `--batch <id>` is used with `--attempted`, `batch` is emitted immediately after `docked` for CavityScore and PoseFeat and contains the supplied id for every row from that publish run, including both docked rows and `docked=False` no-pose rows. This column is not emitted without `--batch` and is never emitted for LigandScore.


Schema meanings are summarized from `SupraEngineering/src/features_lib.py` and
`SupraEngineering/src/constants.py`. Local feature-code docstrings: Feature computation for CB[7]-guest: the 13 mechanism scores and the 24-dim pose pose_features: Returns (vec24 in POSE_FEATURES order, derived dict for mechanism scores). finalize_pose_vec: Fill PC-dependent + tpsa-dependent fields and the PoseScore (Pose_Energy).

## Join key

`inchikey` is the sole join key for `SupraDB-GEOM`, `SupraDB-LigandScore`,
`SupraDB-PoseFeat`, and `SupraDB-CavityScore`. Downstream loaders should join on
this column and treat the feature values as produced by the pipeline order in
`constants.SCORE_NAMES` and `constants.POSE_FEATURES`.

## Provenance

- Pipeline position: Phase 1 docked-subset GLIDE feature computation by compute_all_features; Phase 2 publish split.
- Source pickle: `data/dock_b8_pull/scores.pkl`.
- Computation environment: CRC.
- Docking/software context: GLIDE 2025u2 / aISS fallback as documented in the integration spec.
- Pose collapse: pose-collapse=highest Boltzmann weight; PoseFeat keeps the real pose at `np.argmax(boltz)` and records its `boltzmann_weight` and `delta_e`.
- Row count: 5000.
- Exact publish.py command: `/Users/billyma/Workspace/applications/SupraDashboard/.claude/worktrees/ui-beautify/app/.venv/bin/python engineering/src/publish.py --dock-scores data/dock_b8_pull/scores.pkl --pose-feats data/dock_b8_pull/pose_feats.pkl --identity data/dock_b8_pull/identity.csv --attempted data/dock_b8_pull/guests.csv --batch batch8 --out data/publish_b8`.

## Regeneration

Regenerate this dataset by rerunning `SupraEngineering/src/publish.py` with the same
pipeline pickle input and `--out` target. Use `--push` only in an authenticated
environment with `HF_TOKEN` set; local generation is fully offline by default.