File size: 3,996 Bytes
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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.
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