---
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.