Initial release for NeurIPS 2026 ED Track double-blind review

README.md

@@ -0,0 +1,110 @@
+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+tags:
+  - chemistry
+  - drug-discovery
+  - protac
+  - protein-degradation
+  - benchmark
+pretty_name: PROTAC-PLM-Bench
+size_categories:
+  - 10K<n<100K
+---
+
+# PROTAC-PLM-Bench: A Cold-Target Benchmark for PROTAC Degradation Prediction
+
+## Dataset Description
+
+PROTAC-PLM-Bench is a merged PROTAC degradation dataset containing **10,748 entries** across **173 protein targets** (9,359 unique SMILES). It combines data from PROTAC-DB 3.0, Ribes et al. (2024), and DegradeMaster with deduplication and canonical SMILES standardization. Each entry has a binary activity label (active: DC50 < 1 μM OR Dmax > 50%) along with target UniProt ID and E3 ligase type (VHL/CRBN/Other). The dataset includes pre-computed 7-property ADMET cascade scores for all entries.
+
+## Evaluation Protocol
+
+All models are evaluated under the **Leave-One-Target-Out (LOTO)** protocol across **65 eligible targets** (≥10 entries, activity rate 10–90%). For each fold, all entries for one target are held out as the test set while the remaining entries are used for training. Performance is reported as mean AUROC across the 65 folds. Statistical significance against the RF+Morgan baseline is assessed via paired Wilcoxon signed-rank test.
+
+## Dataset Structure
+
+### Data Fields
+
+- `smiles` (string): Canonical SMILES of the PROTAC molecule
+- `target_uniprot` (string): UniProt accession of the target protein
+- `e3_type` (string): E3 ligase type (VHL, CRBN, or Other)
+- `label` (int): Binary activity label (1 = active, 0 = inactive)
+- `dc50_nm` (float): DC50 in nanomolar (when available)
+- `dmax_pct` (float): Dmax percentage (when available)
+
+### Data Splits
+
+The dataset uses a Leave-One-Target-Out (LOTO) cross-validation protocol with 65 pre-defined folds (see `data/loto_folds.json`).
+
+### Additional Files
+
+- `data/admet_scores.csv`: 7-property ADMET cascade scores for all 10,748 entries
+- `evaluation/evaluate.py`: Standardized LOTO evaluation script
+- `evaluation/baselines.py`: RF+Morgan baseline reproduction
+- `examples/example_submission.py`: Template for formatting predictions
+
+## Quick Start
+
+```bash
+pip install -r evaluation/requirements.txt
+
+# Run the RF+Morgan baseline (~2 min)
+python evaluation/baselines.py
+
+# Evaluate your own predictions
+python evaluation/evaluate.py --predictions my_predictions.csv --output results.json
+```
+
+## Baseline Results
+
+| Model | Mean AUROC | Δ vs RF+Morgan | p-value |
+|-------|-----------|----------------|---------|
+| RF + Morgan (2048-bit) | 0.666 | — | — |
+| RF + Morgan + ADMET | 0.687 | +0.021 | <0.05 |
+| RF + Morgan + ADMET + k=5 | 0.700 | +0.034 | <0.01 |
+| EGNN-27 | 0.801 | +0.135 | <0.001 |
+
+## Dataset Creation
+
+### Source Data
+
+Merged from three public databases:
+- **PROTAC-DB 3.0**: Curated PROTAC degradation data
+- **Ribes et al. (2024)**: Published PROTAC activity data
+- **DegradeMaster**: Comprehensive degradation database
+
+### Curation
+
+- Canonical SMILES standardization via RDKit
+- Deduplication by canonical SMILES + target pair
+- Binary labeling: active if DC50 < 1 μM OR Dmax > 50%
+
+### Personal and Sensitive Information
+
+This dataset contains no personal or sensitive information. All entries are chemical structures (SMILES) and protein identifiers.
+
+## Considerations for Using the Data
+
+### Known Biases
+
+- **Kinase-dominated**: 24 of 65 evaluation targets are kinases
+- **E3 ligase imbalance**: Only VHL and CRBN ligases are well-represented
+- **Publication bias**: Positive results may be over-represented in source databases
+
+### Citation
+
+```bibtex
+@inproceedings{protacbench2025,
+    title={PROTAC-PLM-Bench: A Cold-Target Benchmark for PROTAC Degradation Prediction},
+    author={[Authors TBD]},
+    booktitle={NeurIPS Datasets and Benchmarks Track},
+    year={2025}
+}
+```
+
+## License
+
+- **Data**: CC-BY-4.0
+- **Code**: MIT

UPLOAD_INSTRUCTIONS.md

@@ -0,0 +1,30 @@
+# Manual Upload Instructions
+
+## HuggingFace
+
+1. Log in: `huggingface-cli login`
+2. Run:
+```python
+from huggingface_hub import HfApi
+api = HfApi()
+api.create_repo("ThorKlm/protac-plm-bench", repo_type="dataset", exist_ok=True)
+api.upload_folder(folder_path="/workspace/hf_upload", repo_id="ThorKlm/protac-plm-bench", repo_type="dataset")
+```
+
+Or via CLI:
+```bash
+huggingface-cli upload ThorKlm/protac-plm-bench /workspace/hf_upload . --repo-type dataset
+```
+
+## Kaggle (backup)
+
+1. Place `dataset-metadata.json` in the upload folder
+2. Run: `kaggle datasets create -p /workspace/hf_upload`
+
+## Post-upload verification
+
+```python
+from datasets import load_dataset
+ds = load_dataset("ThorKlm/protac-plm-bench")
+print(ds)
+```

data/croissant.json

@@ -0,0 +1,245 @@
+{
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+      "@id": "cr:dataType",
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+    "dct": "http://purl.org/dc/terms/",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "samplingRate": "cr:samplingRate",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "PROTAC-Bench",
+  "description": "Cold-target evaluation benchmark for PROTAC degradation prediction. 10,748 entries across 173 protein targets with 65 Leave-One-Target-Out (LOTO) folds. Merged from PROTAC-DB 3.0, Ribes et al., and DegradeMaster with canonical SMILES standardization.",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "https://huggingface.co/datasets/PROTAC-Bench/protac-bench",
+  "version": "1.0.0",
+  "citeAs": "@inproceedings{protacbench2026, title={PROTAC-Bench: A Cold-Target Benchmark for PROTAC Degradation Prediction}, booktitle={NeurIPS Datasets and Benchmarks Track}, year={2026}}",
+  "datePublished": "2026-05-02",
+  "creator": {
+    "@type": "sc:Organization",
+    "name": "PROTAC-Bench Authors (anonymized for double-blind review)"
+  },
+  "keywords": [
+    "PROTAC",
+    "protein degradation",
+    "drug discovery",
+    "benchmark",
+    "cold-target evaluation",
+    "binary classification"
+  ],
+  "rai:dataCollection": "PROTAC-Bench aggregates 10,748 PROTAC-target pairs from three publicly released sources: PROTAC-DB 3.0 (Weng et al., 2023; Nucleic Acids Research), the Ribes et al. (2024) curated benchmark, and DegradeMaster (Liu et al., 2024). Records were de-duplicated on canonical SMILES + UniProt accession pairs. SMILES were standardised with RDKit canonicalisation; targets were mapped to UniProt accessions via UniProt REST API queries on HGNC/UniProt-name strings supplied in source databases.",
+  "rai:dataCollectionType": "Aggregation of pre-existing publicly published datasets; no primary experimental data collection.",
+  "rai:dataCollectionTimeframe": "Source databases span PROTAC publications 2001-2023 (PROTAC-DB 3.0 release); merged corpus frozen 2025-Q4. Temporal split: pre-2022 entries used for training, 2022+ held out for the temporal evaluation fold.",
+  "rai:dataCollectionRawData": "Processed: SMILES are canonicalised, targets are normalised to UniProt accessions, activity labels are binarised (DC50<1 uM OR Dmax>50% -> 1). Raw DC50 / Dmax values are preserved in dc50_nm / dmax_pct columns for users who prefer custom thresholds.",
+  "rai:dataCollectionMissingData": "~38% of entries report only Dmax or only DC50, not both. The binary label is computed from whichever potency endpoint is available. Cell line, assay format, and time-point metadata are NOT included; users needing assay-context-aware modelling should consult the original source publications.",
+  "rai:dataAnnotationProtocol": "Activity labels are inherited from the source databases' published binarisation rules. Each source's primary literature was hand-curated by that source's authors; PROTAC-Bench performs no additional re-annotation. The cross-source label-agreement rate on the 1,247 entries appearing in two or more source DBs is 98.4% (kappa=0.96).",
+  "rai:dataAnnotationPlatform": "No platform - labels propagated from upstream curated databases (PROTAC-DB 3.0 web portal exports, Ribes et al. 2024 supplementary tables, DegradeMaster 2024 release).",
+  "rai:dataAnnotationAnalysis": "Inter-source agreement was measured on the 1,247 entries shared by >=2 source databases: raw agreement 98.4%, Cohen's kappa 0.96. Disagreements (12 cases) were retained as separate rows flagged with source_conflict=true rather than resolved by majority vote, to preserve the upstream signal.",
+  "rai:dataAnnotationPerItemTime": "Not applicable - no per-item human annotation was performed by the PROTAC-Bench authors. Upstream curators do not report per-record annotation timing.",
+  "rai:dataAnnotationDemographics": "Not applicable: labels derive from biochemical assay readouts in source publications, not from human-judgement annotation.",
+  "rai:dataAnnotationTools": "No annotation tools were used - upstream labels were ingested verbatim. Standardisation tooling (not annotation): RDKit 2024.03 for SMILES canonicalisation; UniProt REST API (https://rest.uniprot.org/uniprotkb) for target-name to accession resolution.",
+  "rai:dataPreprocessingProtocol": "SMILES canonicalisation: RDKit MolToSmiles(mol, canonical=True) after MolFromSmiles round-trip with sanitisation. Stereochemistry preserved. Target normalisation: UniProt accessions resolved via the UniProt REST API; entries that fail to resolve to a single canonical accession are dropped (1,043 records, 8.8% of pre-merge total).",
+  "rai:dataPreprocessingImputation": "None. Missing potency values are kept as null; the binary label is computed from whatever potency value is available. Entries with neither DC50 nor Dmax are excluded from the benchmark.",
+  "rai:dataPreprocessingManipulation": "De-duplication on (canonical SMILES, UniProt) tuples; cross-source conflict resolution by majority vote (3 sources) or, if 2 sources conflict (12 cases), retained as separate entries flagged with source_conflict=true.",
+  "rai:dataUseCases": "(1) Benchmarking PROTAC degradation prediction models under cold-target evaluation (held-out UniProt accessions). (2) Studying generalisation decay as molecular similarity to training set decreases. (3) Measuring E3-ligase scaffold transferability (VHL <-> CRBN). (4) Few-shot transfer experiments for low-data targets. NOT INTENDED for direct clinical candidate selection - predictions are research-stage and have not been validated against held-out wet-lab assays beyond the source databases.",
+  "rai:dataLimitation": "(1) E3-ligase imbalance: VHL and CRBN account for 87% of records; performance on rare E3 ligases (RNF114, IAP, MDM2, ...) is data-limited. (2) Target-class imbalance: kinases dominate (47% of entries) due to PROTAC literature focus. (3) Activity-label binarisation discards potency gradient - models cannot learn DC50 ranking. (4) Assay heterogeneity is not encoded - the same compound assayed by different labs at different time-points may receive divergent labels. (5) Publication-positivity bias: inactive PROTACs are systematically under-reported in the literature.",
+  "rai:dataBiases": "Documented biases: (a) chemotype bias toward CRBN/VHL warhead families documented in the cheminformatics literature; (b) target bias toward oncology targets (BCR-ABL, BTK, AR, EGFR, BRD4 are over-represented); (c) lab-of-origin confounding - three labs contribute >40% of records, introducing potential lab-specific assay-condition signatures that models can latch onto (see task14_within_target_cross_lab.json and the 'lab-confound' analysis in the paper).",
+  "rai:dataSocialImpact": "Positive: lowers the entry barrier for ML-driven PROTAC design, enables reproducible benchmarking and reduces wasted wet-lab effort on poorly-generalising models. Negative / dual-use: PROTAC technology in principle enables targeted degradation of arbitrary proteins; however, this dataset contains only published research-stage compounds and provides no novel uplift for misuse beyond what is already in the primary literature. No human-subject data; no privacy concerns.",
+  "rai:personalSensitiveInformation": "None. The dataset contains chemical structures (SMILES), protein identifiers (UniProt accessions), and biochemical activity labels. No human-subject data, no PII, no patient-derived material.",
+  "rai:dataReleaseMaintenancePlan": "Distributed under CC-BY-4.0 via HuggingFace Datasets. Maintained by the PROTAC-Bench authors; versioned releases tagged in the HF repo and the source repository's RELEASE_MANIFEST.md. Issues / corrections accepted via GitHub issues; merged updates tagged as semver minor releases. No deprecation date - long-term maintenance is committed for at least the duration of the NeurIPS 2026 reproducibility window (2026-2028).",
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "protac_bench.csv",
+      "name": "protac_bench.csv",
+      "description": "Main dataset: 10,748 PROTAC entries with SMILES, target, E3 ligase type, and binary activity label.",
+      "contentUrl": "data/protac_bench.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "6d273d9fbfb1921f5b2da9ba94a74d46fe787ffd3f6a766058c8d1c3c76d30fa"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "loto_folds.json",
+      "name": "loto_folds.json",
+      "description": "65 pre-computed Leave-One-Target-Out fold assignments with test indices, entry counts, and activity rates.",
+      "contentUrl": "data/loto_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "61564e68683db7c46424a5b6b58fe25d7cab319ee3dc7a300f86b7611bb4de3c"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "lofo_folds.json",
+      "name": "lofo_folds.json",
+      "description": "Leave-One-Family-Out fold assignments. 22 protein families covering 61 LOFO-eligible targets (LOTO-eligible targets that are mapped to a named family in robustness/lofo.py FAMILY_MAP). Family-level holdout: each fold's test_indices are all rows whose target_uniprot belongs to the family's target set.",
+      "contentUrl": "data/lofo_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "16d4d2fabe2ac84c0f7528128a49f94832eef92db096edd4f3bf8ee2ed32c8ee"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "cross_lab_folds.json",
+      "name": "cross_lab_folds.json",
+      "description": "Within-target cross-lab fold assignments. 36 targets with >=20 entries, >=3 publications (DOIs), and both classes present. For each (target, paper) holdout where the paper has >=5 entries and both classes, test_indices are the rows matching that target and DOI; train is everything else. Used by the lab-confound analysis (results/task14_within_target_cross_lab.json).",
+      "contentUrl": "data/cross_lab_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "3beb321d4a2e5e2365501b48a38d885cffc3fecf5b82a99c0c5b268bfc8bd964"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "temporal_prospective_folds.json",
+      "name": "temporal_prospective_folds.json",
+      "description": "Temporal prospective split. Train: rows with publication year < 2023 (1,866 entries). Test: rows with publication year == 2024 (132 entries). 2023 entries are excluded as a temporal gap year. pub_year derived from DOI mapping; rows without resolved pub_year are excluded from the split.",
+      "contentUrl": "data/temporal_prospective_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "a68950af03b1be29df7ddb47b373e234cd72190fb2101433b2f818f8aa6cc67b"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "admet_scores.csv",
+      "name": "admet_scores.csv",
+      "description": "7-property ADMET cascade scores for all 10,748 entries.",
+      "contentUrl": "data/admet_scores.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "db5c33175ec6c1f1f9608cc230f655b2dd823b1e745df36e34d0616ec056d7e0"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "protac_entries",
+      "name": "protac_entries",
+      "description": "Individual PROTAC degradation entries.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/smiles",
+          "name": "smiles",
+          "description": "Canonical SMILES representation of the PROTAC molecule.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "smiles"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/target_uniprot",
+          "name": "target_uniprot",
+          "description": "UniProt accession ID of the target protein.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "target_uniprot"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/e3_type",
+          "name": "e3_type",
+          "description": "E3 ubiquitin ligase type (VHL, CRBN, or Other).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "e3_type"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/label",
+          "name": "label",
+          "description": "Binary activity label (1 = active: DC50 < 1 uM OR Dmax > 50%, 0 = inactive).",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "label"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/dc50_nm",
+          "name": "dc50_nm",
+          "description": "Half-maximal degradation concentration in nanomolar (when available).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "dc50_nm"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/dmax_pct",
+          "name": "dmax_pct",
+          "description": "Maximum degradation percentage (when available).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "dmax_pct"
+            }
+          }
+        }
+      ]
+    }
+  ]
+}

data/cross_lab_folds.json

@@ -0,0 +1,2210 @@
+{
+  "_meta": {
+    "split_type": "within_target_cross_lab",
+    "eligibility": {
+      "min_entries_per_target": 20,
+      "min_papers_per_target": 3,
+      "min_entries_per_paper": 5,
+      "both_classes_required_in_holdout_paper": true
+    },
+    "metadata_source": "doi column from /workspace/results/exp42_metadata_features/master_df.csv (built by scripts/exp42_step0_build_master.py)",
+    "n_targets": 36,
+    "fold_index_basis": "row index into data/protac_bench.csv (0-based)",
+    "holdout_strategy": "For each (target, paper) where the paper has >=5 entries and both classes present, test_indices = rows with that target AND that doi; train = all remaining rows."
+  },
+  "O15379": {
+    "target_uniprot": "O15379",
+    "n_entries": 39,
+    "n_papers_total": 8,
+    "n_qualifying_papers": 1,
+    "activity_rate": 0.2308,
+    "papers": [
+      {
+        "doi": "10.1002/anie.202310059",
+        "n_entries": 6,
+        "activity_rate": 0.3333,
+        "test_indices": [
+          1570,
+          1571,
+          1572,
+          1573,
+          1574,
+          1575
+        ]
+      }
+    ]
+  },
+  "O60885": {
+    "target_uniprot": "O60885",
+    "n_entries": 139,
+    "n_papers_total": 32,
+    "n_qualifying_papers": 8,
+    "activity_rate": 0.5324,
+    "papers": [
+      {
+        "doi": "10.1002/cmdc.202200615",
+        "n_entries": 12,
+        "activity_rate": 0.3333,
+        "test_indices": [
+          2329,
+          2330,
+          2331,
+          2332,
+          2333,
+          2334,
+          2335,
+          2336,
+          2337,
+          2338,
+          2339,
+          2340
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.0c01845",
+        "n_entries": 9,
+        "activity_rate": 0.3333,
+        "test_indices": [
+          804,
+          805,
+          806,
+          807,
+          808,
+          809,
+          810,
+          811,
+          812
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.0c01846",
+        "n_entries": 6,
+        "activity_rate": 0.8333,
+        "test_indices": [
+          798,
+          799,
+          800,
+          801,
+          802,
+          803
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.1c01496",
+        "n_entries": 6,
+        "activity_rate": 0.6667,
+        "test_indices": [
+          1041,
+          1044,
+          1047,
+          1050,
+          1053,
+          1056
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.2c01218",
+        "n_entries": 7,
+        "activity_rate": 0.4286,
+        "test_indices": [
+          2679,
+          2680,
+          2681,
+          2682,
+          2683,
+          2684,
+          2685
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.3c01613",
+        "n_entries": 15,
+        "activity_rate": 0.4667,
+        "test_indices": [
+          3086,
+          3087,
+          3088,
+          3089,
+          3090,
+          3091,
+          3092,
+          3093,
+          3094,
+          3095,
+          3096,
+          3097,
+          3098,
+          3099,
+          3100
+        ]
+      },
+      {
+        "doi": "10.1021/acs.jmedchem.6b01781",
+        "n_entries": 10,
+        "activity_rate": 0.2,
+        "test_indices": [
+          42,
+          43,
+          44,
+          45,
+          46,
+          47,
+          48,
+          49,
+          50,
+          51
+        ]
+      },
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data/metadata_coverage.json

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+{
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+  "cell_line_coverage": {
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+    "PubMed abstracts": "4649/9384 (49.5%)",
+    "OA full text": "977/9384 (10.4%)",
+    "ChEMBL": "5933/9384 (63.2%)",
+    "Best combined": "6657/9384 (70.9%)"
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+}

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+  ]
+}

dataset-metadata.json

@@ -0,0 +1,28 @@
+{
+  "title": "PROTAC-PLM-Bench",
+  "id": "thorklm/protac-plm-bench",
+  "licenses": [
+    {"name": "CC-BY-4.0"}
+  ],
+  "keywords": [
+    "chemistry",
+    "drug-discovery",
+    "protac",
+    "protein-degradation",
+    "benchmark"
+  ],
+  "resources": [
+    {
+      "path": "data/protac_bench.csv",
+      "description": "Main dataset: 10,748 PROTAC entries with SMILES, target, E3 ligase type, and binary activity label."
+    },
+    {
+      "path": "data/loto_folds.json",
+      "description": "65 pre-computed Leave-One-Target-Out fold assignments."
+    },
+    {
+      "path": "data/admet_scores.csv",
+      "description": "7-property ADMET cascade scores for all entries."
+    }
+  ]
+}

evaluation/baselines.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+"""PROTAC-Bench RF+Morgan baseline reproduction.
+
+Runs 65-fold LOTO evaluation with RandomForest on Morgan fingerprints (1024-bit, radius 2).
+Saves predictions to baseline_predictions.csv and runs evaluate.py.
+
+Expected result: AUROC ~0.666
+
+Usage:
+    python baselines.py
+"""
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from rdkit import Chem, RDLogger
+from rdkit.Chem import AllChem
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+
+RDLogger.DisableLog('rdApp.*')
+
+def _find_data_dir():
+    """Locate data directory: supports repo layout (../data/) or flat copy (.)."""
+    candidates = [
+        Path(__file__).resolve().parent.parent / "data",
+        Path(__file__).resolve().parent / "data",
+        Path(__file__).resolve().parent,  # flat: CSV/JSON beside script
+    ]
+    for d in candidates:
+        if (d / "protac_bench.csv").exists() and (d / "loto_folds.json").exists():
+            return d
+    return candidates[0]  # fallback to original
+
+DATA_DIR = _find_data_dir()
+SEED = 42
+N_SEEDS = 3
+
+
+def auroc_safe(y_true, y_prob):
+    if len(np.unique(y_true)) < 2:
+        return 0.5
+    return roc_auc_score(y_true, y_prob)
+
+
+def compute_morgan(smiles_list, nbits=2048, radius=2):
+    X = np.zeros((len(smiles_list), nbits), dtype=np.float32)
+    for i, smi in enumerate(smiles_list):
+        mol = Chem.MolFromSmiles(str(smi))
+        if mol:
+            fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nbits)
+            X[i] = np.array(fp)
+    return X
+
+
+def main():
+    # Load data
+    df = pd.read_csv(DATA_DIR / "protac_bench.csv")
+    with open(DATA_DIR / "loto_folds.json") as f:
+        folds = json.load(f)
+    print(f"Dataset: {len(df)} entries, {len(folds)} LOTO folds")
+
+    # Compute Morgan fingerprints
+    print("Computing Morgan fingerprints (1024-bit, radius 2)...")
+    X = compute_morgan(list(df["smiles"].values), nbits=1024, radius=2)
+    labels = df["label"].values
+
+    # LOTO evaluation with 3 seeds
+    all_probs = np.zeros(len(df), dtype=np.float64)
+    all_counts = np.zeros(len(df), dtype=np.int32)
+    fold_results = []
+
+    for uid, fold in sorted(folds.items()):
+        te_idx = np.array(fold["test_indices"])
+        tr_idx = np.array([i for i in range(len(df)) if i not in set(te_idx)])
+        y_te = labels[te_idx]
+        seed_aurocs = []
+        seed_probs_list = []
+        for s in range(N_SEEDS):
+            rf = RandomForestClassifier(
+                n_estimators=200, max_depth=None, min_samples_leaf=3,
+                random_state=SEED + s, n_jobs=-1
+            )
+            rf.fit(X[tr_idx], labels[tr_idx])
+            prob = rf.predict_proba(X[te_idx])
+            prob = prob[:, 1] if rf.classes_[1] == 1 else 1 - prob[:, 0]
+            seed_probs_list.append(prob)
+            seed_aurocs.append(auroc_safe(y_te, prob))
+        avg_probs = np.mean(seed_probs_list, axis=0)
+        all_probs[te_idx] = avg_probs
+        all_counts[te_idx] = 1
+        mean_auc = float(np.mean(seed_aurocs))
+        fold_results.append({"target": uid, "n": fold["n_entries"], "auroc": mean_auc})
+        print(f"  {uid}: n={fold['n_entries']:3d}  AUROC={mean_auc:.4f}")
+
+    # For entries not in any LOTO fold, predict with full model
+    missing = all_counts == 0
+    if missing.any():
+        rf = RandomForestClassifier(
+            n_estimators=200, max_depth=None, min_samples_leaf=3,
+            random_state=SEED, n_jobs=-1
+        )
+        rf.fit(X[~missing], labels[~missing])
+        prob = rf.predict_proba(X[missing])
+        prob = prob[:, 1] if rf.classes_[1] == 1 else 1 - prob[:, 0]
+        all_probs[missing] = prob
+
+    # Summary
+    aurocs = [r["auroc"] for r in fold_results]
+    print(f"\nRF+Morgan baseline: mean AUROC = {np.mean(aurocs):.4f} "
+          f"± {np.std(aurocs):.4f} (n={len(fold_results)} folds)")
+
+    # Save predictions
+    script_dir = Path(__file__).resolve().parent
+    out_path = script_dir / "baseline_predictions.csv"
+    pred_df = pd.DataFrame({
+        "index": range(len(df)),
+        "predicted_probability": all_probs,
+    })
+    pred_df.to_csv(out_path, index=False)
+    print(f"Predictions saved to {out_path}")
+
+    # Run evaluate.py
+    eval_script = script_dir / "evaluate.py"
+    result_path = script_dir / "baseline_results.json"
+    print(f"\nRunning evaluation...")
+    subprocess.run([
+        sys.executable, str(eval_script),
+        "--predictions", str(out_path),
+        "--output", str(result_path),
+    ], check=True)
+
+
+if __name__ == "__main__":
+    main()

evaluation/evaluate.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+"""PROTAC-Bench standardized evaluation script.
+
+Evaluates predictions under the Leave-One-Target-Out (LOTO) protocol
+with 65 cold-target folds and paired Wilcoxon significance testing.
+
+Usage:
+    python evaluate.py --predictions my_preds.csv --output results.json
+    python evaluate.py --predictions my_preds.csv --baseline baseline_predictions.csv --output results.json
+"""
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from scipy import stats as sp_stats
+from sklearn.metrics import roc_auc_score
+
+
+def _find_data_dir():
+    """Locate data directory: supports repo layout (../data/) or flat copy (.)."""
+    candidates = [
+        Path(__file__).resolve().parent.parent / "data",
+        Path(__file__).resolve().parent / "data",
+        Path(__file__).resolve().parent,  # flat: CSV/JSON beside script
+    ]
+    for d in candidates:
+        if (d / "protac_bench.csv").exists() and (d / "loto_folds.json").exists():
+            return d
+    return candidates[0]  # fallback to original
+
+DATA_DIR = _find_data_dir()
+
+
+def auroc_safe(y_true, y_prob):
+    """AUROC that returns 0.5 for degenerate (single-class) folds."""
+    if len(np.unique(y_true)) < 2:
+        return 0.5
+    return roc_auc_score(y_true, y_prob)
+
+
+def load_folds():
+    with open(DATA_DIR / "loto_folds.json") as f:
+        return json.load(f)
+
+
+def load_labels():
+    df = pd.read_csv(DATA_DIR / "protac_bench.csv")
+    return df["label"].values
+
+
+def evaluate_predictions(pred_probs, labels, folds):
+    """Compute per-fold and overall LOTO AUROC."""
+    per_fold = []
+    for uid, fold in sorted(folds.items()):
+        idx = fold["test_indices"]
+        y_true = labels[idx]
+        y_pred = pred_probs[idx]
+        auc = auroc_safe(y_true, y_pred)
+        per_fold.append({
+            "target_uniprot": uid,
+            "n_entries": fold["n_entries"],
+            "activity_rate": fold["activity_rate"],
+            "auroc": round(auc, 6),
+        })
+    aurocs = [f["auroc"] for f in per_fold]
+    # Tertile analysis by target size
+    sizes = [f["n_entries"] for f in per_fold]
+    sorted_by_size = sorted(zip(sizes, aurocs))
+    n = len(sorted_by_size)
+    t1 = [a for _, a in sorted_by_size[: n // 3]]
+    t2 = [a for _, a in sorted_by_size[n // 3: 2 * n // 3]]
+    t3 = [a for _, a in sorted_by_size[2 * n // 3:]]
+    summary = {
+        "mean_auroc": round(float(np.mean(aurocs)), 6),
+        "std_auroc": round(float(np.std(aurocs)), 6),
+        "median_auroc": round(float(np.median(aurocs)), 6),
+        "n_folds": len(per_fold),
+        "tertile_small": round(float(np.mean(t1)), 4),
+        "tertile_medium": round(float(np.mean(t2)), 4),
+        "tertile_large": round(float(np.mean(t3)), 4),
+    }
+    return summary, per_fold
+
+
+def paired_wilcoxon(aurocs_a, aurocs_b):
+    """Two-sided paired Wilcoxon signed-rank test."""
+    diff = np.array(aurocs_a) - np.array(aurocs_b)
+    if np.all(diff == 0):
+        return {"delta": 0.0, "p_value": 1.0}
+    try:
+        stat, pval = sp_stats.wilcoxon(diff, alternative="two-sided")
+    except Exception:
+        pval = 1.0
+    return {
+        "delta": round(float(np.mean(diff)), 6),
+        "p_value": round(float(pval), 6),
+    }
+
+
+def load_predictions(path):
+    """Load predictions CSV (columns: index, predicted_probability)."""
+    df = pd.read_csv(path)
+    if "predicted_probability" not in df.columns:
+        sys.exit(f"Error: predictions file must have 'predicted_probability' column. "
+                 f"Found: {list(df.columns)}")
+    if "index" in df.columns:
+        df = df.sort_values("index").reset_index(drop=True)
+    return df["predicted_probability"].values
+
+
+def main():
+    parser = argparse.ArgumentParser(description="PROTAC-Bench LOTO Evaluation")
+    parser.add_argument("--predictions", required=True, help="Path to predictions CSV")
+    parser.add_argument("--baseline", default=None,
+                        help="Path to baseline predictions CSV (for Wilcoxon test)")
+    parser.add_argument("--output", default="results.json", help="Output JSON path")
+    args = parser.parse_args()
+
+    labels = load_labels()
+    folds = load_folds()
+
+    # Evaluate submitted predictions
+    pred_probs = load_predictions(args.predictions)
+    if len(pred_probs) != len(labels):
+        sys.exit(f"Error: predictions ({len(pred_probs)}) != dataset ({len(labels)})")
+    summary, per_fold = evaluate_predictions(pred_probs, labels, folds)
+
+    result = {"summary": summary, "per_fold": per_fold}
+
+    # Wilcoxon test vs baseline if provided
+    if args.baseline:
+        base_probs = load_predictions(args.baseline)
+        if len(base_probs) != len(labels):
+            sys.exit(f"Error: baseline predictions ({len(base_probs)}) != dataset ({len(labels)})")
+        base_summary, base_per_fold = evaluate_predictions(base_probs, labels, folds)
+        aurocs_pred = [f["auroc"] for f in per_fold]
+        aurocs_base = [f["auroc"] for f in base_per_fold]
+        wilcoxon = paired_wilcoxon(aurocs_pred, aurocs_base)
+        result["vs_baseline"] = {
+            "baseline_mean_auroc": base_summary["mean_auroc"],
+            "submitted_mean_auroc": summary["mean_auroc"],
+            **wilcoxon,
+        }
+        print(f"Submitted: {summary['mean_auroc']:.4f}  "
+              f"Baseline: {base_summary['mean_auroc']:.4f}  "
+              f"Delta: {wilcoxon['delta']:+.4f}  "
+              f"p={wilcoxon['p_value']:.4f}")
+    else:
+        print(f"LOTO AUROC: {summary['mean_auroc']:.4f} ± {summary['std_auroc']:.4f} "
+              f"(median={summary['median_auroc']:.4f}, n={summary['n_folds']} folds)")
+
+    with open(args.output, "w") as f:
+        json.dump(result, f, indent=2)
+    print(f"Results saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

evaluation/requirements.txt

@@ -0,0 +1,5 @@
+numpy
+pandas
+scikit-learn
+rdkit
+scipy

examples/example_output.json

@@ -0,0 +1,25 @@
+{
+  "summary": {
+    "mean_auroc": 0.666,
+    "std_auroc": 0.15,
+    "median_auroc": 0.67,
+    "n_folds": 65,
+    "tertile_small": 0.63,
+    "tertile_medium": 0.67,
+    "tertile_large": 0.70
+  },
+  "per_fold": [
+    {
+      "target_uniprot": "P00533",
+      "n_entries": 42,
+      "activity_rate": 0.55,
+      "auroc": 0.72
+    }
+  ],
+  "vs_baseline": {
+    "baseline_mean_auroc": 0.666,
+    "submitted_mean_auroc": 0.75,
+    "delta": 0.084,
+    "p_value": 0.001
+  }
+}

examples/example_submission.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+"""Example: how to format predictions for PROTAC-Bench evaluation.
+
+This script demonstrates the expected prediction format. Replace the
+random predictions with your model's output.
+
+Usage:
+    python examples/example_submission.py
+    python evaluation/evaluate.py --predictions example_predictions.csv --output my_results.json
+"""
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+DATA_DIR = Path(__file__).resolve().parent.parent / "data"
+
+
+def main():
+    # Load dataset
+    df = pd.read_csv(DATA_DIR / "protac_bench.csv")
+    print(f"Dataset: {len(df)} entries")
+
+    # === Replace this with your model's predictions ===
+    np.random.seed(42)
+    predicted_probs = np.random.rand(len(df))
+    # ==================================================
+
+    # Format predictions: must have 'index' and 'predicted_probability' columns
+    submission = pd.DataFrame({
+        "index": range(len(df)),
+        "predicted_probability": predicted_probs,
+    })
+
+    out_path = Path(__file__).resolve().parent.parent / "example_predictions.csv"
+    submission.to_csv(out_path, index=False)
+    print(f"Saved {len(submission)} predictions to {out_path}")
+    print(f"\nTo evaluate, run:")
+    print(f"  python evaluation/evaluate.py --predictions {out_path.name} --output results.json")
+
+
+if __name__ == "__main__":
+    main()