| --- |
| 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`: `float32` NumPy array shaped `(10, n_heavy, 3)` containing |
| ten original Cartesian conformers. Coordinate atom `i` matches `atoms[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 |
|
|
| ```text |
| 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 |
|
|
| ```python |
| 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. |
|
|
