Add GenomeScreen as five normalized parquet tables

README.md

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 ---
-license: mit
+license: cc-by-4.0
+tags:
+- virtual-screening
+- drug-discovery
+- aidd
+pretty_name: GenomeScreen (relational)
+configs:
+- config_name: targets
+  data_files: targets.parquet
+- config_name: pockets
+  data_files: pockets.parquet
+- config_name: pocket_structures
+  data_files: pocket_structures.parquet
+- config_name: molecules
+  data_files: molecules.parquet
+- config_name: hits
+  data_files: hits.parquet
 ---
+
+# GenomeScreen, as a relational dataset
+
+A normalized, queryable form of [GenomeScreen](https://huggingface.co/datasets/THU-ATOM/GenomeScreenDB)
+— the DrugCLIP genome-wide virtual screen of human proteins. The source release ships as
+~26.5k per-pocket directories of CSV and structure files; this is the same data parsed into
+five parquet tables.
+
+**This contains the screen results only.** The 27 GB of `.pdbgz` receptor structures are not
+redistributed here; `pocket_structures` records their paths relative to the source release's
+`screen_results/` directory so you can join against your own copy.
+
+## Tables
+
+| file | rows | grain |
+| --- | ---: | --- |
+| `targets.parquet` | 9,919 | UniProt accession |
+| `pockets.parquet` | 26,562 | screen-result directory (target × AF fragment × pocket) |
+| `pocket_structures.parquet` | 215,481 | refined receptor conformation + docking grid |
+| `molecules.parquet` | 1,110,392 | distinct `(oid, smiles)` — 982,239 distinct compounds |
+| `hits.parquet` | 3,175,634 | `(pocket, molecule)` DrugCLIP hit |
+
+Join keys: `hits.pocket_key → pockets.pocket_key → targets.uniprot_acc`, and
+`hits.mol_id → molecules.mol_id`.
+
+## How the data is keyed
+
+- **Targets are UniProt accessions**, wrapped in AlphaFold DB ids. A `pocket_key` of
+  `AF-Q12879-F1-model_v4_0_pocket3` is UniProt `Q12879`, AF2 model fragment `0`, pocket `3`.
+- **Pockets come in two kinds.** `pocket_kind = 'detected'` (22,061) were found by apo pocket
+  detection; `'template'` (4,501) were transferred from an aligned holo structure and keep the
+  source PDB id in `pocket_structures.template_pdb_id` (3,125 distinct entries).
+- **Molecules are vendor catalogue ids plus SMILES — there are no PubChem CIDs.** `oid` is a
+  ZINC id (`ZINC000066055208`), an Enamine REAL id (`Z1333761449_1_T2`) or an Enamine PV id
+  (`PV-001914042032_1_T1`). The Enamine `_<protomer>_T<tautomer>` suffix is split into
+  `protomer_idx` / `tautomer_idx`; `catalog_id` strips it.
+- **The same `oid` sometimes carries two SMILES** (protonation variants), so the `molecules`
+  grain is `(oid, smiles)` behind a surrogate `mol_id`. Group by `catalog_id` to collapse them.
+- **`hits.source_index` is not a molecule id.** It is the source `leader.csv` `Name` column, a
+  row index into the screened library that differs between targets for the same compound. Use
+  `catalog_id` to identify a compound. `rank_in_pocket` (1 = best) is derived from `score`.
+
+## Caveats worth knowing before you use it
+
+- **These are predicted hits, not measured interactions.** `score` is DrugCLIP similarity.
+- **Scores are not calibrated across proteins.** Ranking is meaningful within a pocket; a
+  global threshold across targets is not.
+- **The per-molecule view is truncated by construction.** The screen kept each *pocket's* top
+  10K, then cluster leaders — so a molecule's known target is not guaranteed to survive that
+  cut. Empirically it often doesn't: ibuprofen appears with 34 predicted targets, none of them
+  COX-1 or COX-2, though both are screened. Read a molecule's row set as a biased sample of
+  its profile, not as its profile.
+- **It is a make-on-demand screening library, not a drug library.** Most approved drugs are
+  absent entirely.
+
+## Usage
+
+```python
+import duckdb
+conn = duckdb.connect()
+conn.execute("""
+    CREATE VIEW hits    AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/hits.parquet';
+    CREATE VIEW pockets AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/pockets.parquet';
+    CREATE VIEW molecules AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/molecules.parquet';
+""")
+
+# top hits for a UniProt accession, one row per compound
+conn.execute("""
+    SELECT m.catalog_id, m.source, max(h.score) AS score,
+           arg_max(m.smiles, h.score) AS smiles
+    FROM hits h
+    JOIN pockets   p ON p.pocket_key = h.pocket_key
+    JOIN molecules m ON m.mol_id = h.mol_id
+    WHERE p.uniprot_acc = ?
+    GROUP BY 1, 2 ORDER BY score DESC LIMIT 25
+""", ["P14416"]).fetchall()
+```
+
+## Source and license
+
+Derived from **GenomeScreen** by Jia et al., released under CC-BY-4.0. This derived dataset
+carries the same license, and the original work must be credited:
+
+> Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H.,
+> Huang, Y., Deng, Z., Jin, Y., Yuan, Y., Tian, J., He, W., Ma, W., Zhang, Y., Yan, C., Liu, L.,
+> Zhang, W., Lan, Y. *Deep contrastive learning enables genome-wide virtual screening.*
+> bioRxiv 2024.09.02.610777.
+
+- Source dataset: https://huggingface.co/datasets/THU-ATOM/GenomeScreenDB
+- Portal: https://drug-the-whole-genome.yanyanlan.com
+- Code: https://github.com/THU-ATOM/Drug-The-Whole-Genome
+
+```bibtex
+@article{Jia2024GenomeScreen,
+  title={Deep contrastive learning enables genome-wide virtual screening},
+  author={Jia, Yinjun and Gao, Bowen and Tan, Jiaxin and Zheng, Jiqing and Hong, Xin and Zhu, Wenyu and Tan, Haichuan and Xiao, Yuan and Tan, Liping and Cai, Hongyi and Huang, Yanwen and Deng, Zhiheng and Jin, Yue and Yuan, Yafei and Tian, Jiekang and He, Wei and Ma, Weiying and Zhang, Yaqin and Yan, Chuangye and Liu, Lei and Zhang, Wei and Lan, Yanyan},
+  journal={bioRxiv},
+  year={2024},
+  doi={10.1101/2024.09.02.610777}
+}
+```

hits.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2860dcacb7111d3b4ab23ee5b34c4969c5f7570d4b920df869982bd506182870
+size 40081870

molecules.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5fcb41c6367898de22b92add9e29ed61946c220faeef7f8d9f26d9d5fed813b
+size 23521514

pocket_structures.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7353b158d3d119d23da6bed0453d41021b52a69ffe90d3f8dbe66ea78208f42
+size 8075622

pockets.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:757e76ed21335406d789e65eaa3988d682aff90cb7c9b037b3342bf309724473
+size 498381

targets.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56a9a8f7185e7a330b23da85a71feae2c4265c85f246b59cf9f07ffa6471cbc6
+size 127928