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| Format: | Recurso digital |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.15572019 |
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| _version_ | 1866901828050878464 |
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| author | Smith, Zachary Strobel, Michael Vani, Bodhi Tiwary, Pratyush |
| author_facet | Smith, Zachary Strobel, Michael Vani, Bodhi Tiwary, Pratyush |
| contents | <p>The modified version of the COACH420 dataset that was used for GrASP evaluation.</p> <ol> <li><strong>unprocessed_pdb.zip</strong>: PDB structures from the original COACH420 dataset.</li> <li><strong>ready_to_parse_mol2.zip</strong>: Protein and ligand structures after our additional processing was applied.</li> <li><strong>raw.zip</strong>: NumPy arrays of the features used to construct PyTorch Geometric graphs.</li> <li><strong>processed.zip</strong>: Processed protein graphs used as graph neural network inputs.</li> <li><strong>mol2.zip</strong>: Protein with hydrogens removed and atoms renumbered accordingly. Indices match the node feature order in the NumPy and PyTorch files.</li> <li><strong>coach420(mlig)_uniprot.pkl</strong>: Pickle containing UniProt ID for each receptor, used to define train/test splits.</li> </ol> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15572019 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Graph Attention Site Prediction (GrASP) COACH420 Dataset Smith, Zachary Strobel, Michael Vani, Bodhi Tiwary, Pratyush <p>The modified version of the COACH420 dataset that was used for GrASP evaluation.</p> <ol> <li><strong>unprocessed_pdb.zip</strong>: PDB structures from the original COACH420 dataset.</li> <li><strong>ready_to_parse_mol2.zip</strong>: Protein and ligand structures after our additional processing was applied.</li> <li><strong>raw.zip</strong>: NumPy arrays of the features used to construct PyTorch Geometric graphs.</li> <li><strong>processed.zip</strong>: Processed protein graphs used as graph neural network inputs.</li> <li><strong>mol2.zip</strong>: Protein with hydrogens removed and atoms renumbered accordingly. Indices match the node feature order in the NumPy and PyTorch files.</li> <li><strong>coach420(mlig)_uniprot.pkl</strong>: Pickle containing UniProt ID for each receptor, used to define train/test splits.</li> </ol> |
| title | Graph Attention Site Prediction (GrASP) COACH420 Dataset |
| url | https://doi.org/10.5281/zenodo.15572019 |