README.md

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

# SupraDB-LigandScore

## What it is

`SupraBench/SupraDB-LigandScore` is a SupraBench feature dataset generated from the
SupraEngineering compute pipeline. Pipeline position: Phase 1 whole-pool ligand-only scoring by score_nodock; 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. |
| `S_charge` | float32 | unitless score | Ligand charge score combining formal/partial positive charge localization; score_nodock sets pose charge accessibility to zero. |
| `S_hydrophobic` | float32 | unitless score | Hydrophobicity score from logP, hydrophobic volume, hydrophobic SASA fraction, and hydrophobic atom fraction. |
| `S_rigidity` | float32 | unitless score | Ligand rigidity score derived from rotatable-bond count. |
| `S_desolvation` | float32 | unitless score | Ligand desolvation favorability score derived from topological polar surface area. |
| `S_packing` | float32 | unitless score | Ligand packing score from molecular volume relative to the CB[7] cavity volume. |
| `S_shape` | float32 | unitless score | Ligand shape score from radius of gyration, asphericity, and compactness. |
| `S_conformer_diversity` | float32 | unitless score | Conformer concentration score favoring low conformer diversity. |
| `S_boltzmann_concentration` | float32 | unitless score | Boltzmann concentration score from the top conformer ensemble weight mass. |
| `S_bad` | float32 | unitless score | Penalty-like score for anionic groups, high polarity, flexibility, and asphericity. |

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 whole-pool ligand-only scoring by score_nodock; Phase 2 publish split.
- Source pickle: `data/pool_full/features/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: 429140.
- Exact publish.py command: `/users/tma2/workspace/SupraDashboard/.venv/bin/python engineering/src/publish.py --pool-scores data/pool_full/features/scores.pkl --identity data/pool_full/guests.csv --out data/pub_full`.

## 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.