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Main Authors: Smith, Zachary, Strobel, Michael, Vani, Bodhi, Tiwary, Pratyush
Format: Recurso digital
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Published: Zenodo 2024
Online Access:https://doi.org/10.5281/zenodo.15572019
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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
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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