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README.md

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+---
+pretty_name: BioAssayAlign Assay-Compound Data
+tags:
+- biology
+- chemistry
+- drug-discovery
+- bioassay
+- screening
+- ranking
+- parquet
+language:
+- en
+license: other
+size_categories:
+- 100M<n<1B
+---
+
+# BioAssayAlign Assay-Compound Data
+
+<p align="center">
+  <img src="./bioassayalign.png" alt="BioAssayAlign logo" width="280">
+</p>
+
+## What this dataset is
+
+BioAssayAlign Assay-Compound Data is a **frozen assay-and-molecule dataset for assay-conditioned ranking and retrieval**.
+
+It answers questions like:
+- given an assay description, which molecules in a submitted list should rank first?
+- which historical assays are closest to this assay?
+
+It is not:
+- a chatbot dataset
+- a generic pretraining corpus
+- a clinical or patient dataset
+
+Companion model:
+- [BioAssayAlign Qwen3-Embedding-0.6B Compatibility](https://huggingface.co/lighteternal/BioAssayAlign-Qwen3-Embedding-0.6B-Compatibility)
+
+Companion Space:
+- [BioAssayAlign Compatibility Explorer](https://huggingface.co/spaces/lighteternal/BioAssayAlign-Compatibility-Explorer)
+
+## What is included
+
+This public release has two layers.
+
+### 1. Sanitized frozen raw release
+
+Directory: `raw/`
+
+Files:
+- `raw/assays.parquet`
+- `raw/measurements.parquet`
+- `raw/DATASET_MANIFEST.json`
+
+These are the public, sanitized versions of the frozen corpus derived from:
+- PubChem BioAssay snapshot dated `2026-03-01`
+- ChEMBL release `chembl_36`
+
+### 2. Prepared compatibility-ranking subset
+
+Directory: `prepared/compatibility-ranking/`
+
+Files:
+- `compat_assays.parquet`
+- `compat_candidate_pools.parquet`
+- `compat_train_groups.parquet`
+- `COMPATIBILITY_PREPARED_MANIFEST.json`
+- `SOURCE_DATASET_MANIFEST.json`
+
+This prepared subset is the one used to train the published compatibility model linked above.
+
+## Why there are multiple parquet files
+
+### `raw/assays.parquet`
+One row per assay. Contains:
+- assay text
+- assay metadata
+- normalized target identifiers
+
+### `raw/measurements.parquet`
+One row per assay-compound measurement. Contains:
+- assay-compound links
+- standardized SMILES
+- activity labels
+- potency-like fields when available
+
+### `prepared/compatibility-ranking/compat_assays.parquet`
+Prepared assay rows used for compatibility ranking.
+
+### `prepared/compatibility-ranking/compat_candidate_pools.parquet`
+Held-out assay candidate pools used for evaluation.
+
+### `prepared/compatibility-ranking/compat_train_groups.parquet`
+Training groups with:
+- one assay
+- one positive molecule
+- explicit same-assay negative molecules
+
+## Dataset scale
+
+### Sanitized frozen release
+
+| File | Rows |
+|---|---:|
+| `raw/assays.parquet` | `3,800,882` |
+| `raw/measurements.parquet` | `323,706,180` |
+
+### Prepared ranking subset used by the public model
+
+| File | Rows |
+|---|---:|
+| `compat_assays.parquet` | `11,195` |
+| `compat_candidate_pools.parquet` | `1,432,532` |
+| `compat_train_groups.parquet` | `508,216` |
+
+Split counts:
+
+| Split | Assays |
+|---|---:|
+| train | `8,967` |
+| val | `1,117` |
+| test | `1,111` |
+
+## Sanitization and privacy
+
+This public dataset does **not** contain patient data or direct personal identifiers.
+
+Before release, I removed internal-only provenance details such as:
+- local filesystem paths
+- internal shard/precompute artifacts
+- private training-only intermediate files
+
+This public repo intentionally excludes:
+- shard directories from HF CPU prep jobs
+- precomputed training feature stores
+- internal benchmark artifacts unrelated to the released model
+
+## File schemas
+
+### `raw/assays.parquet`
+
+Important columns:
+- `assay_uid`
+- `source`
+- `assay_id`
+- `title`
+- `description_text`
+- `organism`
+- `readout`
+- `assay_format`
+- `assay_type`
+- `target_uniprot`
+- `metadata_json`
+- `provenance_json`
+
+### `raw/measurements.parquet`
+
+Important columns:
+- `assay_uid`
+- `compound_uid`
+- `canonical_smiles`
+- `smiles_hash`
+- `activity_label`
+- `activity_type`
+- `activity_value`
+- `activity_units`
+- `p_activity`
+- `relation`
+- `confidence`
+- `metadata_json`
+- `provenance_json`
+
+### `prepared/compatibility-ranking/compat_train_groups.parquet`
+
+Important columns:
+- `assay_uid`
+- `positive_smiles`
+- `positive_smiles_hash`
+- `negative_smiles`
+- `negative_smiles_hashes`
+- `example_weight`
+
+This is the core ranking supervision format used by the public model.
+
+## Example row
+
+Conceptually, one training observation looks like:
+
+```json
+{
+  "assay_uid": "pubchem:720659",
+  "positive_smiles": "CC1=CC(=O)N(C)C(=O)N1",
+  "positive_smiles_hash": "4d6f0d...abc",
+  "negative_smiles": [
+    "CCOC1=CC=CC=C1",
+    "CCN(CC)CCOC1=CC=CC=C1",
+    "COC1=CC=CC=C1O"
+  ],
+  "negative_smiles_hashes": [
+    "a1...",
+    "b2...",
+    "c3..."
+  ],
+  "example_weight": 1.34
+}
+```
+
+## How to load it locally
+
+### Python / pandas
+
+```python
+import pandas as pd
+
+assays = pd.read_parquet("raw/assays.parquet")
+measurements = pd.read_parquet("raw/measurements.parquet")
+train_groups = pd.read_parquet("prepared/compatibility-ranking/compat_train_groups.parquet")
+```
+
+### Python / pyarrow
+
+```python
+import pyarrow.parquet as pq
+
+assays = pq.read_table("raw/assays.parquet")
+measurements = pq.read_table("raw/measurements.parquet")
+```
+
+## How this relates to the public model
+
+The published model was trained on:
+- `prepared/compatibility-ranking/compat_assays.parquet`
+- `prepared/compatibility-ranking/compat_candidate_pools.parquet`
+- `prepared/compatibility-ranking/compat_train_groups.parquet`
+
+Published model:
+- [lighteternal/BioAssayAlign-Qwen3-Embedding-0.6B-Compatibility](https://huggingface.co/lighteternal/BioAssayAlign-Qwen3-Embedding-0.6B-Compatibility)
+
+## Upstream sources
+
+This dataset is derived from public upstream resources including:
+- PubChem BioAssay
+- ChEMBL
+
+Users are responsible for complying with the attribution and usage terms of the upstream sources.