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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
compound_id: string
smiles_input: string
smiles_standardized: string
source: string
passed_mpo_filter: bool
mw: double
logp: double
tpsa: double
hbd: int64
hba: int64
heavy_atoms: int64
fsp3: double
cns_mpo_score: double
efflux_MDR1_HUMAN: double
efflux_A4D1D2_HUMAN: double
efflux_ABCG2_HUMAN: double
efflux_MRP1_HUMAN: double
efflux_MRP2_HUMAN: double
efflux_MRP4_HUMAN: double
efflux_S47A1_HUMAN: double
efflux_S22A8_HUMAN: double
p_bbb: double
heuristic_reason: string
heuristic_veto: bool
score_total: double
provenance: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 3301
to
{'compound_id': Value('string'), 'smiles_input': Value('string'), 'smiles_standardized': Value('string'), 'source': Value('string'), 'provenance': Value('string'), 'passed_mpo_filter': Value('bool'), 'mw': Value('float64'), 'logp': Value('float64'), 'tpsa': Value('float64'), 'hbd': Value('float64'), 'hba': Value('float64'), 'heavy_atoms': Value('float64'), 'fsp3': Value('float64'), 'pka_representative': Value('float64'), 'cns_mpo_score': Value('float64'), 'p_bbb': Value('float64'), 'score_total': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
compound_id: string
smiles_input: string
smiles_standardized: string
source: string
passed_mpo_filter: bool
mw: double
logp: double
tpsa: double
hbd: int64
hba: int64
heavy_atoms: int64
fsp3: double
cns_mpo_score: double
efflux_MDR1_HUMAN: double
efflux_A4D1D2_HUMAN: double
efflux_ABCG2_HUMAN: double
efflux_MRP1_HUMAN: double
efflux_MRP2_HUMAN: double
efflux_MRP4_HUMAN: double
efflux_S47A1_HUMAN: double
efflux_S22A8_HUMAN: double
p_bbb: double
heuristic_reason: string
heuristic_veto: bool
score_total: double
provenance: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 3301
to
{'compound_id': Value('string'), 'smiles_input': Value('string'), 'smiles_standardized': Value('string'), 'source': Value('string'), 'provenance': Value('string'), 'passed_mpo_filter': Value('bool'), 'mw': Value('float64'), 'logp': Value('float64'), 'tpsa': Value('float64'), 'hbd': Value('float64'), 'hba': Value('float64'), 'heavy_atoms': Value('float64'), 'fsp3': Value('float64'), 'pka_representative': Value('float64'), 'cns_mpo_score': Value('float64'), 'p_bbb': Value('float64'), 'score_total': Value('float64')}
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BBB-Nuke: 924M Compound Blood-Brain Barrier Permeability Screen
Overview
924 million small molecules screened for blood-brain barrier (BBB) permeability using the BBB-Nuke pipeline.
Data Sources
| Source | Files | Compounds |
|---|---|---|
| Enamine REAL | 2,000 | ~199M |
| Existing (ZINC/ChEMBL) | 7,384 | ~666M |
| PubChem | 625 | ~59M |
| Total | 10,009 | 924,220,136 |
Key Columns
- Canonical SMILES
- BBB permeability probability (0-1)
- CNS multi-parameter optimization score
- , , - Key physicochemical descriptors
- Predicted pKa
- Data source (enamine_real, existing, pubchem)
Compute
Screened on Azure ML using 8x NVIDIA A100 80GB GPUs (Standard_ND96amsr_A100_v4).
Citation
If you use this dataset, please cite the ATTN-Lab BBB-Nuke project.
License
CC BY-NC 4.0
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