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
frame_rule: string
special_entries: int64
errors: int64
dropped_collinear: int64
source: string
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
frame_rule: string
special_entries: int64
errors: int64
dropped_collinear: int64
source: string
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 Ligands 100M
This dataset contains 100,000,000 globally unique heavy-atom molecular records split across five LMDB shards. Each shard contains 20,000,000 records and all records are assigned to the training split.
Record schema
Each numeric LMDB key contains a pickled Python dictionary with:
smi: canonical heavy-atom molecule encoded as SELFIES.atoms: heavy-atom symbols in decoded SELFIES atom order.coordinates:float32NumPy array shaped(10, n_heavy, 3)containing ten original Cartesian conformers. Coordinate atomimatchesatoms[i].qed: RDKit QED.sa_score: RDKit Contrib synthetic accessibility score.molecular_weight: RDKit average molecular weight.mol_log_p: RDKit MolLogP.tpsa: RDKit topological polar surface area.
Explicit hydrogen atoms and hydrogen coordinates are not included. Standard implicit hydrogens are used by RDKit when calculating molecular properties.
Files
ligands/shard_1.lmdb
ligands/shard_1_metadata.json
...
ligands/shard_5.lmdb
ligands/shard_5_metadata.json
ligands/valid.lmdb
ligands/valid_metadata.json
Every shard_*.lmdb stores numeric keys b"0" through b"19999999" and a
pickled b"length" value equal to 20_000_000.
The local deduplication registries used during generation are not uploaded; they are not needed to train from the finalized records.
valid.lmdb uses the same record schema and contains 10,000 unique
SELFIES generated from unused source slice 6. All 10,000 entries were checked
against the finalized training registries and have no overlap with the 100M
training records. Its numeric keys are b"0" through b"9999", and its
pickled b"length" value is 10_000.
Loading
import lmdb
import pickle
env = lmdb.open(
"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"))
Pickle should only be loaded from a trusted dataset source.
Uniqueness
SELFIES were deduplicated exactly within each shard and separated across shards by SHA-256 hash ownership. All five finalized registries contained exactly 20,000,000 entries with no registry-only extras, establishing 100,000,000 unique SELFIES records in total.
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