The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
count: int64
excluded_overlength_count: int64
excluded_overlength_records: list<item: struct<conservative_length: int64, index: int64, key: string>>
child 0, item: struct<conservative_length: int64, index: int64, key: string>
child 0, conservative_length: int64
child 1, index: int64
child 2, key: string
filtered_from_lmdb: string
hydrogens_included: bool
max_conservative_sequence_length: int64
output_lmdb: string
paired_pocket_ligand: bool
pocket_atoms: string
record_schema: list<item: string>
child 0, item: string
rmsd_range_angstrom: struct<max_exclusive: double, min_inclusive: double>
child 0, max_exclusive: double
child 1, min_inclusive: double
selection: string
source_lmdb: string
source_record_count: int64
split: string
split_seed: int64
vina_score_max: double
vina_score_mean: double
vina_score_min: double
vina_score_std: double
vina_scores_present: bool
special_entries: int64
source: string
frame_rule: string
dropped_collinear: int64
errors: int64
to
{'count': Value('int64'), 'dropped_collinear': Value('int64'), 'errors': Value('int64'), 'frame_rule': Value('string'), 'source': Value('string'), 'special_entries': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
count: int64
excluded_overlength_count: int64
excluded_overlength_records: list<item: struct<conservative_length: int64, index: int64, key: string>>
child 0, item: struct<conservative_length: int64, index: int64, key: string>
child 0, conservative_length: int64
child 1, index: int64
child 2, key: string
filtered_from_lmdb: string
hydrogens_included: bool
max_conservative_sequence_length: int64
output_lmdb: string
paired_pocket_ligand: bool
pocket_atoms: string
record_schema: list<item: string>
child 0, item: string
rmsd_range_angstrom: struct<max_exclusive: double, min_inclusive: double>
child 0, max_exclusive: double
child 1, min_inclusive: double
selection: string
source_lmdb: string
source_record_count: int64
split: string
split_seed: int64
vina_score_max: double
vina_score_mean: double
vina_score_min: double
vina_score_std: double
vina_scores_present: bool
special_entries: int64
source: string
frame_rule: string
dropped_collinear: int64
errors: int64
to
{'count': Value('int64'), 'dropped_collinear': Value('int64'), 'errors': Value('int64'), 'frame_rule': Value('string'), 'source': Value('string'), 'special_entries': Value('int64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MolWeaver Pretraining Data
This repository contains the LMDB datasets used for MolWeaver molecular and
protein-ligand pretraining. All data is stored under one top-level
final_data/ directory.
Layout
final_data/
└── mol_data/
└── data/
├── ligands/
├── pockets_center_frame_cartesian/
└── crossdock/
Free ligands
final_data/mol_data/data/ligands/ contains five heavy-atom molecular LMDB
training shards. Shards 1–4 contain 20 million records each. The final 3,000
records were removed from shard 5 and stored in valid.lmdb, leaving
19,997,000 records in the active fifth training shard.
Each ligand record contains a SELFIES representation, heavy-atom identities, ten aligned Cartesian conformers, and five RDKit-derived properties: QED, synthetic accessibility score, molecular weight, MolLogP, and TPSA.
Pockets
final_data/mol_data/data/pockets_center_frame_cartesian/ contains protein
pocket coordinates represented in a deterministic heavy-atom Cartesian frame.
The training LMDB contains 3,123,776 records.
CrossDock
final_data/mol_data/data/crossdock/ contains paired heavy-atom pocket-ligand
records with ligand SELFIES, pocket and ligand coordinates, and Vina docking
scores. The training split contains 326,896 records after excluding seven
examples whose conservative sequence length exceeded 4,356. The validation
split contains 3,000 records.
Loading an LMDB
import lmdb
import pickle
env = lmdb.open(
"final_data/mol_data/data/ligands/shard_1.lmdb",
readonly=True,
subdir=False,
lock=False,
readahead=False,
)
with env.begin() as txn:
length = pickle.loads(txn.get(b"length"))
record = pickle.loads(txn.get(b"0"))
Pickled records should only be loaded from a trusted dataset source.
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