SeanWan05/MolAS-Active-Learning

🧬 MolAS Active Learning — Dataset

Active Learning for Molecular-Docking Algorithm Selection
under Heterogeneous Runtime
Accepted at the NeurIPS 2026 Workshop on AI for Drug Discovery (AI4DD)
Sean Wan · Jiabao Brad Wang · Mustafa Misir — Duke Kunshan University

🤗 Dataset · 💻 Code · 📄 Paper · 📝 OpenReview · 🗃️ Data dictionary · 🔎 Verification

GitHub repository: https://github.com/SeanWan514/MolAS_Active_Learning

HuggingFace repository: https://huggingface.co/datasets/SeanWan05/MolAS-Active-Learning

🔬 Dataset summary

This dataset preserves the inputs and saved experimental artifacts for retrospective, cell-wise active learning in molecular-docking algorithm selection. It contains 3,179 protein–ligand complexes, seven docking engines, fixed five-fold memberships, original molecular features, and acquisition/downstream outputs. The dataset also includes all 3,179 original protein graph files, which are distributed separately from the GitHub code release.

The default benchmark_cells configuration contains 22,253 rows, one per complex–algorithm cell. It is a deterministic reshaping of the unchanged original tables, with original outer-fold assignments. No docking, feature inference, acquisition campaign, or selector training was executed to prepare this release.

✨ Included artifacts

Artifact	Contents
Raw outcomes	Aligned RMSD, PoseBusters-validity ratio, and runtime tables: 3,179 × 7 each
Targets	Continuous acquisition scores; the explicitly reconstructed native MolAS evaluation scores; preserved earlier target serialization
Splits and descriptors	Original five outer folds and four physical descriptor columns
Small features	Original 3,179 × 256 projected cache, its verified row IDs, and 3,179 × 384 ligand embeddings
Protein graphs	3,179 original PyTorch/PyG graph files; 14,985,036,224 bytes
Acquisition artifacts	90 run archives covering six policies × five folds × three acquisition seeds; 27,000 original CSV identities
Downstream artifacts	4,508 original CSV payloads in six ZIPs and 1,771 readable summary mirrors; acquisition seed 0
Research context	Paper PDF/source/figures, source-code mirror, data dictionary, historical provenance, and portable inspection utilities

The GitHub repository is the primary code reference. The source mirror here is pinned to the GitHub commit recorded in provenance/hf_source_snapshot.json. The acquisition archive uses lossless storage deduplication: earlier cumulative CSVs are exact prefixes of final ledgers, with original byte identities recorded and verified. Extraction utilities recover the original CSV streams; a duplicate multi-gigabyte transport ZIP is unnecessary.

🚀 Inspect the dataset

For the loading/download examples, install datasets and huggingface_hub (python3 -m pip install -r requirements-hub.txt). The standalone file/data validators require only Python’s standard library.

The Dataset Viewer is explicitly configured for nine independent CSV tables. The binary graphs/features and compressed result archives are download assets and are not automatically deserialized by the Viewer. Every configuration has a split named full.

from datasets import load_dataset

repo_id = "SeanWan05/MolAS-Active-Learning"
cells = load_dataset(repo_id, "benchmark_cells", split="full")
runtime = load_dataset(repo_id, "runtime", split="full")
graphs = load_dataset(repo_id, "protein_graph_index", split="full")

Available configurations: benchmark_cells (default), rmsd, pb_ratio, runtime, acquisition_scores, native_evaluation_scores, folds, physical_descriptors, and protein_graph_index. Their paths and row counts are in provenance/hf_viewer_configurations.json.

Default cellwise schema

Column	Meaning
PDB_CCD_ID	Original complex identifier; preserve as a string
algorithm	One of the seven engine IDs listed below
outer_test_fold	Original test-fold membership, integers 0–4
rmsd_angstrom	Original docking RMSD, interpreted in Å by the paper
pb_ratio	Original PoseBusters validity ratio; strict validity requires exact equality to 1
runtime_seconds	Original runtime, treated as seconds by the experiment code
acquisition_strict_score	Original continuous acquisition target
native_evaluation_score	Native evaluation target from the explicitly named, independently checked reconstruction

Engine order in the original wide matrices: surfdock, unimol, diffdock, diffdock_l, gnina, autodock, smina.

🧬 Download and use the structural inputs

from huggingface_hub import snapshot_download

folder = snapshot_download(
    repo_id="SeanWan05/MolAS-Active-Learning",
    repo_type="dataset",
    local_dir="MolAS_HF",
)

For a versioned analysis, pass the actual published dataset commit to revision=. The graph index provides each graph's relative path, size, and SHA-256 for selective downloading. Original graph files are under data/external/protein_graphs_esmc_600m/; ligand and projected features are under data/features/. Graphs are serialized molecular data, not trained selector weights. Use the preserved MolAS loader and its PyTorch/PyG dependencies rather than treating these binary files as ordinary Dataset Viewer rows.

🔎 Local verification and archive access

From the downloaded folder, Python 3.11+ can validate all file hashes and data consistency without executing scientific experiments:

python3 scripts/verify_hf_package.py
python3 scripts/validate_data.py --require-complete
python3 code/downstream/verify_saved_outputs.py --require-complete --original-archive-manifest provenance/downstream_original_archive_recovery.json

SHA256SUMS and provenance/repository_files.json cover the upload payloads, including all protein graphs. The self-generated manifests and final report are excluded from their own recursive hashes. Verification checks available bytes and recorded provenance; it does not establish a fresh end-to-end reproduction of the historical scientific experiments.

Use scripts/read_acquisition_artifact.py to inspect one original acquisition CSV. Use scripts/materialize_acquisition.py --destination /path/to/new/folder to estimate expansion; add --write to extract. Seed 0 requires about 3.22 GB expanded, and all three acquisition seeds can be selected explicitly. Original downstream CSVs are in the six ZIPs under results/downstream/.

📁 Dataset layout

MolAS_HF/
├── data/                  # raw outcomes, targets, folds, descriptors, and features
│   ├── external/protein_graphs_esmc_600m/  # all 3,179 original graphs
│   └── viewer/            # deterministic cellwise table and graph index
├── results/               # original acquisition and downstream archives/summaries
├── code/                  # pinned source mirror; GitHub remains the code reference
├── paper/                 # unchanged paper, source, bibliography, and figures
├── docs/                  # data dictionary, interpretation, and source attribution
├── provenance/            # source identities, component checks, and HF manifest
├── scripts/               # inspection, validation, and explicit upload helper
├── LICENSE                # component-specific bundle terms
├── LICENSE_DATA.md         # CC BY 4.0 grant for author-owned data contributions
├── LICENSE_CODE           # MIT license for original software
└── UPLOAD.md              # instructions for publishing this prepared folder

⚖️ Licensing and attribution

Author-owned data contributions, original result outputs, and curation metadata are provided under CC BY 4.0, to the extent the authors hold the relevant rights. Original software is MIT. Supplied third-party data, model-derived features/graphs, software, and manuscript assets retain their separately applicable terms. The Hub metadata therefore uses license: other and points to the explicit component-specific LICENSE, rather than assigning a blanket CC BY or MIT license to every bundled artifact. See LICENSE_DATA.md, LICENSE_CODE, and third-party provenance.

Upstream sources include MolAS, its processed-data record, MOAD/PDB-derived complexes, ESM-C protein features, and the recovered ChemBERTa ligand-feature workflow. The author confirmed redistribution terms and delegated selection of the dataset's author-owned-content license; the release record is in provenance/hf_release_authorization.json.

📌 Intended use and interpretation limits

• This is a retrospective reference benchmark. The full tables are offline oracles; acquisition policies may access only previously acquired training-pool outcomes/runtimes. Preserve original held-out folds and warm-start cost accounting.
• Do not randomly split the cellwise rows. The seven rows of one complex share the same original outer-test fold. Derive each training pool from the original complex-level memberships. No protein-family/scaffold-disjoint split is newly asserted.
• Acquisition and native evaluation targets are distinct. Native MolAS evaluation zeroes RMSD >5 Å or <0; the continuous acquisition target does not use that truncation. The original server-side native-target CSV bytes were not recovered; the clearly named reconstruction matches all 22,253 saved engine scores within 1.11e-16.
• Extreme finite RMSD values are preserved, including 211 above 1,000 Å. Their failure/sentinel semantics are not established. They are not evidence of valid physical poses.
• Runtime is hardware/workflow dependent; exact historical timing boundaries and all engine-version details are not established by the numeric tables alone.
• The projected feature cache uses the original fixed full-set normalization/projection. It is not a newly fitted fold-local representation. Runtime-surrogate fitting remains a separate observed-data operation.
• Acquisition artifacts cover three seeds; downstream saved results use acquisition seed 0. Recovered launcher defaults do not establish every historical selector RNG/configuration.
• Per-run training configurations and some final figure-export details remain unpinned. No new experiments, significance tests, generalization studies, or prospective docking validations are claimed by this data release.

📚 Citation

Please cite the active-learning paper and the upstream MolAS/data/feature sources when using their respective components. The manuscript remains the supplied camera-ready PDF; its original availability statement is preserved in this frozen copy.

@inproceedings{wan2026molas_active_learning,
  title = {Active Learning for Molecular-Docking Algorithm Selection under Heterogeneous Runtime},
  author = {Wan, Sean and Wang, Jiabao Brad and Misir, Mustafa},
  booktitle = {NeurIPS Workshop on AI for Drug Discovery},
  year = {2026},
  url = {https://openreview.net/forum?id=tSz3wBLABm}
}

Code: https://github.com/SeanWan514/MolAS_Active_Learning · Data snapshot version: 1.0.0. When citing or reusing the data, record the actual dataset identifier and immutable Hub commit alongside the code version.