conradry/biopharma-hackathon

Biopharma hackathon data

Two independent datasets share this repo. They have different sources and different licenses, and nothing joins them:

1. GenomeScreen (relational) — 5 tables, the DrugCLIP genome-wide virtual screen parsed into parquet.
2. Parkinson's disease subgraph — 5 tables, a pathway-centric neighbourhood extracted from PrimeKG, as a graph and as a disease→pathway→protein→drug tree, plus an environmental-toxin overlay on the same pathways.

---

1. GenomeScreen, as a relational dataset

A normalized, queryable form of GenomeScreen — 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

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

@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}
}

---

2. Parkinson's disease subgraph (PrimeKG)

A pathway-centric neighbourhood of Parkinson's disease extracted from PrimeKG (13,001,666 edges), plus a pathway enrichment table for filtering it down.

file	rows	grain
pd_subgraph.parquet	162,384	edge (both orientations — see below)
pd_subgraph_enrichment.parquet	283	pathway
pd_tree.parquet	54,557	root→leaf path, disease → pathway → protein → drug
pd_toxin_target.parquet	79	(environmental toxin, gene)
pd_toxin_pathway.parquet	76	pathway, with toxin evidence rolled up

How it was built

PrimeKG has no disease_pathway relation, so the disease is bridged to pathways through its associated proteins:

Parkinson disease (x_index 80275)
  --disease_protein-->  104 seed proteins
  --pathway_protein-->  283 pathways
  --pathway_pathway-->  edges among those pathways
  --pathway_protein-->  4,916 proteins in those pathways
  --protein_protein-->  edges among those proteins

The layer column tags which step each edge came from:

layer	rows	unique edges
disease_protein	208	104
pathway_pathway	62	31
pathway_protein	24,544	12,272
protein_protein	137,570	68,785

PD is the merged MONDO/Orphanet node at x_index 80275. The other node named "Parkinson disease" (x_index 91594, MONDO:0005180) carries only disease_disease edges and no protein links.

Columns are PrimeKG's original twelve (relation, display_relation, x_index, x_id, x_type, x_name, x_source, and the y_* equivalents) plus layer, x_pathway_qvalue and y_pathway_qvalue.

Pathway enrichment

Bridging through proteins pulls in generic pathways (Platelet degranulation) alongside PD-relevant ones, so each of the 283 pathways is scored by a hypergeometric test: background = the 10,849 pathway-annotated proteins in PrimeKG, sample = the 90 of 104 seed proteins that carry a pathway annotation. P-values are Benjamini-Hochberg corrected across all 283 pathways. 21 pathways reach q < 0.05.

pd_subgraph_enrichment.parquet holds pathway_index, pathway_name, pathway_size, seed_overlap, expected_overlap, fold_enrichment, pvalue, qvalue, ranked by q-value. The same q-value is denormalized onto the edge table as x_pathway_qvalue / y_pathway_qvalue — populated on whichever endpoint is a pathway, null otherwise (a pathway_pathway edge has both).

Top hits are recognisable PD biology:

pathway	size	overlap	fold	q
Interleukin-4 and Interleukin-13 signaling	108	7	7.8	6.4e-03
Biogenic amines are oxidatively deaminated to aldehydes	2	2	120.5	6.4e-03
Detoxification of Reactive Oxygen Species	37	4	13.0	1.5e-02
Catecholamine biosynthesis	4	2	60.3	1.6e-02
Dopamine receptors	5	2	48.2	2.1e-02
Transcriptional activation of mitochondrial biogenesis	56	4	8.6	2.4e-02

The tree view

pd_tree.parquet is the same neighbourhood reshaped into a tree, with drugs attached below the proteins:

Parkinson disease                    (root)
└── 283 pathways
      └── 4,916 proteins             (12,272 pathway-protein pairs)
            └── 4,993 drugs          (drug_protein edges)

PrimeKG has no drug_pathway relation either, so proteins are the only join between drugs and pathways — a drug hangs off the tree once per (pathway, protein) pair it reaches. Use COUNT(DISTINCT drug_index) when you want unique drugs.

One row per root-to-leaf path, fully denormalized, so any level rolls up with a GROUP BY: disease_index/disease_name, pathway_index/pathway_name/pathway_size/ pathway_seed_overlap/pathway_fold_enrichment/pathway_qvalue, protein_index/ protein_name/protein_is_pd_seed, drug_index/drug_id/drug_name/drug_relation, and depth. Proteins with no drug still get a row (depth = 3, drug columns null; 7,917 of them) so the protein layer stays complete.

Nothing is pre-filtered — the narrowings you are likely to want are all columns: pathway_qvalue < 0.05 (21 pathways), protein_is_pd_seed, drug_relation = 'target'.

drug_relation matters more than it looks. Only target (30,151 rows) is a therapeutic target; enzyme (10,149), transporter (3,680) and carrier (2,660) are ADME relationships — a metabolizing CYP is not a repurposing lead. Note that 185 drug-protein pairs legitimately carry two relation types (usually enzyme + target), so they appear as two rows.

The tree drops protein_protein and pathway_pathway, which have no place in a tree; use pd_subgraph.parquet for those.

The environmental-toxin overlay

A second, independent line of evidence on the same pathways. Ten environmental toxins with an established PD association (paraquat, rotenone, MPTP, manganese, lead, mercury, chlorpyrifos, dieldrin, trichloroethylene, tetrachloroethylene) were mapped to their literature-co-mentioned gene targets, and those genes joined onto the tree's pathways.

The premise: a toxin that is causative for PD implicates the pathways it perturbs, so a pathway reached by both the disease-association route and the toxicological route carries a stronger prior than either alone. 9 of the 21 enriched pathways contain a toxin target, and the top of that list is the canonical PD tox story.

pd_toxin_target.parquet is one row per (toxin, gene): toxin, cid, pubmed_mentions, gene_symbol, protein_index (PrimeKG), evidence_count, n_source_entries, source_entries, mapping_confidence, toxin_mapped_fraction, in_pd_tree, is_pd_seed, tf, idf, specificity_weight.

pd_toxin_pathway.parquet rolls that up per pathway and joins to pd_tree.parquet on pathway_index: n_toxins, n_toxin_targets, toxin_support, toxins, toxin_targets, alongside the pathway's pathway_qvalue, n_drugs and n_target_drugs.

Ranked by toxin support among pathways at q < 0.05:

pathway	support	q	toxins	targets
Detoxification of Reactive Oxygen Species	3.28	1.5e-02	6	CAT, GPX1–3, SOD1–3
Transcriptional activation of mitochondrial biogenesis	2.99	2.4e-02	7	GABPA, PPARGC1B, SOD2
FOXO-mediated transcription of oxidative stress genes	1.98	3.3e-02	6	CAT, SOD2
Interleukin-4 and Interleukin-13 signaling	1.24	6.4e-03	4	AKT1, BCL2, IL6, MAOA, TNF
Catecholamine biosynthesis	1.05	1.6e-02	2	TH
Biogenic amines deaminated by MAOA and MAOB	0.19	6.4e-03	1	MAOA, MAOB

How the targets were mapped

The source lists targets as name [TAG] (count), where TAG is a gene symbol or an EC number. Three properties of that format shape the table:

• The EC entries carry the mechanism. MPTP's causal target is monoamine oxidase, which appears only as EC:1.4.3.4; rotenone's is complex I, only as EC:1.6.99.3. Symbol-only parsing loses both. EC classes are expanded through a hand-curated map, and mapping_confidence records how firm each expansion is: exact (one human gene), family (a small enumerated family — MAOA/MAOB, SOD1–3), or complex (complex I core subunits). Large cross-species classes (peroxidase, unspecific monooxygenase) and non-human ones (photosystem II, lignin peroxidase, bacterial dehalogenases) are dropped rather than guessed at.
• A toxin often lists one protein twice, once by symbol and once by EC — lead has both catalase [CAT] (576) and catalase [EC:1.11.1.6] (234). Rows are aggregated to the (toxin, gene) grain and the counts summed; source_entries preserves what contributed. A family EC has its evidence split across members rather than replicated, so a family tag cannot outvote a specific symbol.
• The gene lists are cross-species. Influenza PB2, bacterial MERA, rice LOC4326471 and a Drosophila GABA receptor all appear. Only symbols resolving to a PrimeKG gene/protein node are kept.

specificity_weight is tf × idf: a gene's share of its toxin's mapped evidence, damped by how many toxins name it. CAT and the SODs appear for 6 of the 10 toxins — they are the background of the toxicology literature, not a toxin-specific signal, and the weighting is what keeps them from dominating.

Caveats worth knowing before you use it

• Every edge in pd_subgraph.parquet appears twice. PrimeKG stores undirected edges in both orientations, and this extract preserves that verbatim — all 162,384 rows are 81,192 unique edges mirrored. Filter x_index < y_index for a single copy, and match on x_type/y_type rather than assuming which column holds the pathway or the disease. (pd_tree.parquet is already deduplicated — it has a fixed level order.)

• The disease→pathway link is inferred, not asserted. These pathways are not curated PD pathways; they are pathways containing at least one PD-associated protein.

• 14 of the 104 seed proteins have no pathway annotation and are excluded from the enrichment test. They remain in the edge table under the disease_protein layer.

• Small pathways produce fragile scores. Several top hits rest on an overlap of 2 in a pathway of size 2–6. Signaling by Insulin receptor at size 2 is a PrimeKG annotation artifact, not the real Reactome pathway. Add pathway_size >= 10 for robust hits only.

• Enrichment is scored over the 283 bridged pathways, not all of PrimeKG. The multiple testing correction is relative to that set.

• Ranking drugs by pathway or target count rewards promiscuity. The query above returns zinc salts, cannabidiol and fatty acids at the top — compounds with dozens of annotated protein interactions, not compounds with a PD rationale. Weight by target specificity, or restrict to a pathway you care about, before reading anything into the ordering.

• PrimeKG's drug nodes are all of DrugBank, and it carries no approval status. Every drug_id is a DB… accession, but the set includes experimental entries, cofactors and crystallographic ligands — NADH, copper and glutathione adducts all appear as target drugs. There is no column to filter on; join against a DrugBank approval list if you need approved compounds only.

Specific to the toxin overlay:

• A shared target does not mean a shared direction of effect, and PrimeKG cannot tell you which. drug_relation distinguishes target from enzyme, never agonist from antagonist. If a toxin causes PD by inhibiting a protein, a drug that also inhibits it is a risk, not a therapy. SLC6A3 is the clearest case: DAT-mediated uptake is how MPP+ enters dopaminergic neurons, so a DAT blocker is protective while amphetamine — on the same target list — is not. Read the overlay as nominating pathways, and get direction from DrugBank's action field before selecting compounds within one.
• The counts are literature co-mentions, not affinities or assays. A high count means the toxin and the gene are frequently discussed together, which tracks how well-studied a gene is at least as much as how relevant it is.
• The source lists only the top 8 of 25 targets per toxin. Document frequencies — and so every idf — are computed over that truncated view, which biases idf upward for genes that fall below the cut for some toxins.
• Two toxins are mostly unmappable. toxin_mapped_fraction is 0.38 for tetrachloroethylene and 0.40 for trichloroethylene; the rest of their lists is bacterial or fungal. Because tf is a share of mapped evidence, the survivors absorb the whole share and their weights are inflated. Treat anything below ~0.5 as soft.
• toxin_support is a raw sum, so it rewards large pathways. Unfiltered, Neutrophil degranulation (478 proteins, q = 0.21) tops the ranking. The enrichment q-value already controls for size — filter on it first, then rank by support.
• 10 toxin targets are absent from the tree entirely, including all seven complex I core subunits — the most PD-relevant mechanism in the set. They resolve to valid PrimeKG protein_index values but sit in no PD-bridged pathway, so they contribute nothing to pd_toxin_pathway.parquet. Filter in_pd_tree = false to see them.

Usage

import duckdb
conn = duckdb.connect()
base = "hf://datasets/conradry/biopharma-hackathon"

# significantly enriched pathways and the proteins in them, deduplicated
conn.execute(f"""
    SELECT DISTINCT
        CASE WHEN x_type = 'pathway' THEN x_name ELSE y_name END AS pathway,
        CASE WHEN x_type = 'pathway' THEN y_name ELSE x_name END AS protein
    FROM '{base}/pd_subgraph.parquet'
    WHERE layer = 'pathway_protein'
      AND coalesce(x_pathway_qvalue, y_pathway_qvalue) < 0.05
      AND x_index < y_index
""").fetchall()

# repurposing candidates: drugs targeting proteins in enriched PD pathways,
# ranked by how many distinct enriched pathways they reach
conn.execute(f"""
    SELECT drug_name,
           count(DISTINCT pathway_index) AS pathways,
           count(DISTINCT protein_index) AS proteins,
           string_agg(DISTINCT protein_name, ', ') AS targets
    FROM '{base}/pd_tree.parquet'
    WHERE drug_relation = 'target' AND pathway_qvalue < 0.05
    GROUP BY 1 ORDER BY pathways DESC, proteins DESC LIMIT 25
""").fetchall()

# convergent evidence: drugs targeting proteins in pathways that are both
# statistically enriched for PD proteins and hit by a PD-associated toxin
conn.execute(f"""
    SELECT t.drug_name, t.protein_name, x.pathway_name,
           x.toxins, x.toxin_support, x.pathway_qvalue
    FROM '{base}/pd_tree.parquet' t
    JOIN '{base}/pd_toxin_pathway.parquet' x USING (pathway_index)
    WHERE t.drug_relation = 'target' AND x.pathway_qvalue < 0.05
    ORDER BY x.toxin_support DESC, t.drug_name
""").fetchall()

Source and license

Derived from PrimeKG by Chandak, Huang and Zitnik. Note that PrimeKG's MIT license covers the software; the upstream repository states that this is distinct from the terms governing the data itself, which is distributed via Harvard Dataverse (doi:10.7910/DVN/IXA7BM) and inherits the terms of its own ~20 constituent primary sources. Check those before redistributing. The license field in this repo's metadata (CC-BY-4.0) describes the GenomeScreen tables above.

The toxin overlay adds a second source: the toxin list and its gene targets come from PubChem (compound records and their literature-co-mentioned genes) and PubMed mention counts, retrieved via the PubChem PUG-REST API. PubChem aggregates from many depositors whose individual terms vary; see https://www.ncbi.nlm.nih.gov/home/about/policies/. The EC-to-gene map and the specificity weighting are original to this dataset.

Chandak, P., Huang, K., Zitnik, M. Building a knowledge graph to enable precision medicine. Nature Scientific Data 10, 67 (2023).

@article{Chandak2023PrimeKG,
  title={Building a knowledge graph to enable precision medicine},
  author={Chandak, Payal and Huang, Kexin and Zitnik, Marinka},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={67},
  year={2023},
  doi={10.1038/s41597-023-01960-3}
}