pretty_name: MolWeaver Ligands 100M
task_categories:
- text-generation
- feature-extraction
tags:
- chemistry
- molecules
- selfies
- conformers
- rdkit
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
shard_1/train.lmdb
shard_1/metadata.json
...
shard_5/train.lmdb
shard_5/metadata.json
Every train.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.
Loading
import lmdb
import pickle
env = lmdb.open(
"shard_1/train.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.