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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'MODEL 1'}) and 3 missing columns ({'rmsd', '# vina_score', 'pdbid'}).

This happened while the csv dataset builder was generating data using

zip://PDBdata/10/10GS-VWW/10GS-VWW_decoys.pdbqt::hf://datasets/YupuZ/DecoyDB@fc5a2b8a3e5574c4b441ab65c1594235f92ca9db/structures.zip

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              MODEL 1: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 376
              to
              {'pdbid': Value(dtype='string', id=None), '# vina_score': Value(dtype='float64', id=None), 'rmsd': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1428, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'MODEL 1'}) and 3 missing columns ({'rmsd', '# vina_score', 'pdbid'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://PDBdata/10/10GS-VWW/10GS-VWW_decoys.pdbqt::hf://datasets/YupuZ/DecoyDB@fc5a2b8a3e5574c4b441ab65c1594235f92ca9db/structures.zip
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

pdbid
string
# vina_score
float64
rmsd
float64
10GS-VWW
-6.849
4.659878
10GS-VWW
-6.829
3.814967
10GS-VWW
-6.669
6.303845
10GS-VWW
-6.594
5.977943
10GS-VWW
-6.588
6.288187
10GS-VWW
-6.416
5.541668
10GS-VWW
-6.363
6.640499
10GS-VWW
-6.359
5.967768
10GS-VWW
-6.35
7.182811
10GS-VWW
-6.277
8.001085
10GS-VWW
-6.265
6.555503
10GS-VWW
-6.185
9.312336
10GS-VWW
-6.176
6.826641
10GS-VWW
-6.058
6.573407
10GS-VWW
-6.012
8.351678
10GS-VWW
-6.009
7.063061
10GS-VWW
-5.961
6.413722
10GS-VWW
-5.926
4.692872
10GS-VWW
-5.916
6.794795
10GS-VWW
-5.907
6.496293
10GS-VWW
-5.885
6.101104
10GS-VWW
-5.755
5.054888
10GS-VWW
-5.747
6.996554
10GS-VWW
-5.723
5.806851
10GS-VWW
-5.651
9.811802
10GS-VWW
-5.605
5.497033
10GS-VWW
-5.585
8.170451
10GS-VWW
-5.564
8.490276
10GS-VWW
-5.555
7.647462
10GS-VWW
-5.545
6.1256
10GS-VWW
-5.535
8.37163
10GS-VWW
-5.508
5.935044
10GS-VWW
-5.489
8.461925
10GS-VWW
-5.473
8.154902
10GS-VWW
-5.454
6.627912
10GS-VWW
-5.441
7.29316
10GS-VWW
-5.418
8.125244
10GS-VWW
-5.398
5.572532
10GS-VWW
-5.362
7.276332
10GS-VWW
-5.354
7.596731
10GS-VWW
-5.313
9.205617
10GS-VWW
-5.281
7.960158
10GS-VWW
-5.28
9.585594
10GS-VWW
-5.275
8.71343
10GS-VWW
-5.258
6.925304
10GS-VWW
-5.247
7.505578
10GS-VWW
-5.233
5.790226
10GS-VWW
-5.226
8.361425
10GS-VWW
-5.217
8.399775
10GS-VWW
-5.213
6.354485
10GS-VWW
-5.205
9.179231
10GS-VWW
-5.204
9.562518
10GS-VWW
-5.203
6.373942
10GS-VWW
-5.197
8.777455
10GS-VWW
-5.196
6.866523
10GS-VWW
-5.186
6.359702
10GS-VWW
-5.177
7.591611
10GS-VWW
-5.175
7.157656
10GS-VWW
-5.155
6.123518
10GS-VWW
-5.15
5.781893
10GS-VWW
-5.123
9.105703
10GS-VWW
-5.123
8.614573
10GS-VWW
-5.119
9.583221
10GS-VWW
-5.119
9.608389
10GS-VWW
-5.106
6.312141
10GS-VWW
-5.103
6.421252
10GS-VWW
-5.09
6.236233
10GS-VWW
-5.084
6.828954
10GS-VWW
-5.081
6.596879
10GS-VWW
-5.081
6.416339
10GS-VWW
-5.067
6.892225
10GS-VWW
-5.058
9.562727
10GS-VWW
-5.052
8.009839
10GS-VWW
-5.049
6.516289
10GS-VWW
-5.036
8.385843
10GS-VWW
-5.032
6.326549
10GS-VWW
-5.022
6.316435
10GS-VWW
-5.015
8.474068
10GS-VWW
-5.007
8.969239
10GS-VWW
-5.001
7.542822
10GS-VWW
-5
8.043035
10GS-VWW
-4.989
9.028502
10GS-VWW
-4.989
8.864614
10GS-VWW
-4.988
9.448008
10GS-VWW
-4.977
8.062363
10GS-VWW
-4.974
7.306424
10GS-VWW
-4.971
9.695047
10GS-VWW
-4.954
7.665555
10GS-VWW
-4.952
6.684864
10GS-VWW
-4.94
8.420167
10GS-VWW
-4.937
6.52071
10GS-VWW
-4.92
7.079571
10GS-VWW
-4.918
6.508571
10GS-VWW
-4.917
6.532794
10GS-VWW
-4.907
7.125179
10GS-VWW
-4.906
9.009592
10GS-VWW
-4.895
5.558379
10GS-VWW
-4.882
8.119142
10GS-VWW
-4.864
6.683162
10GS-VWW
-4.831
5.532565
End of preview.

🔧Code, 📂Dataset

Dataset Summary

DecoyDB is a curated dataset of high-resolution protein-ligand complexes and their associated decoy structures. It is designed to support research on graph contrastive learning, binding affinity prediction, and structure-based drug discovery. The dataset is derived from experimentally resolved complexes and refined to ensure data quality.

Data Structure

Each protein-ligand complex is stored in a nested directory under DecoyDB/, using the format:

DecoyDB
├── README.md                                              # This file
├── merged_decoy_scores.csv                                # RMSD and Vina score for all decoys
├── structures.zip                                         # Structures for proteins, ligands and decoys
  ├── {prefix}/                                            # {prefix} = first 2 characters of the complex ID (e.g., '1A', '2B')
  │   └── {complex_id}/                                    # Unique identifier for each complex (e.g., 1A2C_H1Q)
  │       ├── {complex_id}_ligand.pdbqt                    # Ligand structure in AutoDock format
  │       ├── {complex_id}_target.pdbqt                    # Protein structure in AutoDock format
  │       ├── {complex_id}_decoys.pdbqt                    # Concatenated decoy structures
  │       └── {complex_id}_decoys_scores.csv               # Corresponding RMSD scores for each decoy

Dataset Details

Dataset Refinement

To construct DecoyDB, we first filtered protein–ligand complexes from the Protein Data Bank (PDB) with a resolution ≤ 2.5 Å and applied the following refinement steps:

  • Removed ligands with molecular weights outside the (50, 1000) range.
  • Excluded complexes involving metal clusters, monoatomic ions, and common crystallization molecules.
  • Retained ligands with elements limited to C, N, O, H, S, P, and halogens.
  • Retained those protein chains with at least one atom within 10 Å of the ligand.
  • Saved the ligand and protein separately.

Decoy Generation

For each refined protein–ligand complex, 100 decoy poses were generated using AutoDock Vina 1.2, with a 5 Å padding grid box and an exhaustiveness parameter of 8 and remove unrealistic generated structures.

Dataset Statistics

  • Number of protein–ligand complexes: 61,104
  • Number of decoys: 5,353,307
  • Average number of decoys per complex: 88
  • Average RMSD: 7.22 Å
  • RMSD range: [0.03, 25.56] Å

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