Add dataset card

README.md

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+---
+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
+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
+
+```python
+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.