GEOM-QM9 & GEOM-DRUGS preprocessed conformer pickles (Align Your Structures, ICLR 2026)
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| Natura: | Recurso digital |
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2026
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| _version_ | 1866901639009402880 |
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| author | Iyengar, Aniketh Han, Jiaqi Sun, Pengwei Jiang, Mingjian Xie, Jianwen Ermon, Stefano |
| author_facet | Iyengar, Aniketh Han, Jiaqi Sun, Pengwei Jiang, Mingjian Xie, Jianwen Ermon, Stefano |
| contents | <p>Preprocessed GEOM-QM9 and GEOM-DRUGS conformer pickles used for conformer-level diffusion pretraining (Stage 1) in <em>Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics</em> (ICLR 2026).</p><p><strong>Contents (6 pickles, ~3 GB uncompressed):</strong></p><ul><li><code>GEOM-QM9_Train.pkl</code> — QM9 train split</li><li><code>GEOM-QM9_Val.pkl</code> — QM9 validation split</li><li><code>GEOM-QM9_Test_Actual_compat.pkl</code> — QM9 test split (ConfGF 200-molecule official benchmark, repackaged)</li><li><code>GEOM-DRUGS_Train.pkl</code> — DRUGS train split</li><li><code>GEOM-DRUGS_Val.pkl</code> — DRUGS validation split</li><li><code>GEOM-DRUGS_Test_Actual_compat.pkl</code> — DRUGS test split (ConfGF 200-molecule official benchmark, repackaged)</li></ul><p><strong>Format:</strong> each pickle is a Python <code>list</code> of <code>dict</code>s with keys <code>atom_type</code>, <code>boltzmannweight</code>, <code>edge_index</code>, <code>edge_type</code>, <code>idx</code>, <code>nx</code>, <code>pos</code>, <code>rdmol</code>, <code>smiles</code>, <code>totalenergy</code>. Same fields as GeoDiff's PyTorch-Geometric <code>Data</code> format, repackaged as dicts to decouple loading from PyG version drift.</p><p><strong>Provenance:</strong> train / val derived from the GeoDiff preprocessed GEOM archive (<a href="https://github.com/MinkaiXu/GeoDiff">MinkaiXu/GeoDiff</a>), itself built on top of the ConfGF preprocessing pipeline (<a href="https://github.com/DeepGraphLearning/ConfGF">DeepGraphLearning/ConfGF</a>). Test split is ConfGF's official 200-molecule benchmark. Upstream raw GEOM: Axelrod & Gómez-Bombarelli, <em>Scientific Data</em> 2022, doi:10.7910/DVN/JNGTDF.</p><p><strong>Extraction:</strong></p><pre>tar xf align-your-structures-conformer-pkls-v1.tar.gz -C ${MD_DATA_ROOT}/</pre><p>The archive expands into <code>processed_input_data/GEOM-{QM9,DRUGS}/</code>, matching the paths referenced in the <code>configs_official/</code> YAMLs.</p><p><strong>Reference code:</strong> <a href="https://github.com/ani11452/Align_Your_Structures">https://github.com/ani11452/Align_Your_Structures</a></p><p>If you use this data please cite the paper above and the upstream GEOM dataset.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19711753 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | GEOM-QM9 & GEOM-DRUGS preprocessed conformer pickles (Align Your Structures, ICLR 2026) Iyengar, Aniketh Han, Jiaqi Sun, Pengwei Jiang, Mingjian Xie, Jianwen Ermon, Stefano molecular conformers GEOM-QM9 GEOM-DRUGS diffusion models generative models ICLR 2026 <p>Preprocessed GEOM-QM9 and GEOM-DRUGS conformer pickles used for conformer-level diffusion pretraining (Stage 1) in <em>Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics</em> (ICLR 2026).</p><p><strong>Contents (6 pickles, ~3 GB uncompressed):</strong></p><ul><li><code>GEOM-QM9_Train.pkl</code> — QM9 train split</li><li><code>GEOM-QM9_Val.pkl</code> — QM9 validation split</li><li><code>GEOM-QM9_Test_Actual_compat.pkl</code> — QM9 test split (ConfGF 200-molecule official benchmark, repackaged)</li><li><code>GEOM-DRUGS_Train.pkl</code> — DRUGS train split</li><li><code>GEOM-DRUGS_Val.pkl</code> — DRUGS validation split</li><li><code>GEOM-DRUGS_Test_Actual_compat.pkl</code> — DRUGS test split (ConfGF 200-molecule official benchmark, repackaged)</li></ul><p><strong>Format:</strong> each pickle is a Python <code>list</code> of <code>dict</code>s with keys <code>atom_type</code>, <code>boltzmannweight</code>, <code>edge_index</code>, <code>edge_type</code>, <code>idx</code>, <code>nx</code>, <code>pos</code>, <code>rdmol</code>, <code>smiles</code>, <code>totalenergy</code>. Same fields as GeoDiff's PyTorch-Geometric <code>Data</code> format, repackaged as dicts to decouple loading from PyG version drift.</p><p><strong>Provenance:</strong> train / val derived from the GeoDiff preprocessed GEOM archive (<a href="https://github.com/MinkaiXu/GeoDiff">MinkaiXu/GeoDiff</a>), itself built on top of the ConfGF preprocessing pipeline (<a href="https://github.com/DeepGraphLearning/ConfGF">DeepGraphLearning/ConfGF</a>). Test split is ConfGF's official 200-molecule benchmark. Upstream raw GEOM: Axelrod & Gómez-Bombarelli, <em>Scientific Data</em> 2022, doi:10.7910/DVN/JNGTDF.</p><p><strong>Extraction:</strong></p><pre>tar xf align-your-structures-conformer-pkls-v1.tar.gz -C ${MD_DATA_ROOT}/</pre><p>The archive expands into <code>processed_input_data/GEOM-{QM9,DRUGS}/</code>, matching the paths referenced in the <code>configs_official/</code> YAMLs.</p><p><strong>Reference code:</strong> <a href="https://github.com/ani11452/Align_Your_Structures">https://github.com/ani11452/Align_Your_Structures</a></p><p>If you use this data please cite the paper above and the upstream GEOM dataset.</p> |
| title | GEOM-QM9 & GEOM-DRUGS preprocessed conformer pickles (Align Your Structures, ICLR 2026) |
| topic | molecular conformers GEOM-QM9 GEOM-DRUGS diffusion models generative models ICLR 2026 |
| url | https://doi.org/10.5281/zenodo.19711753 |