AutoencoderDataset

Data for training molvae — a SELFIES molecular VAE (matryoshka latent) for Bayesian optimization of mechanophores. Two layers:

• raw/ — the source chemical databases (SMILES/SELFIES, pre-tokenized), as collected.
• mixes/ — the derived training datasets: weighted, shuffled, materialized token-shard "mixes" built from the raw sources. Each mix is documented by its config + README + build code, so it is fully reproducible. The multi-GB token shards themselves live on S3 (s3://mechanophore/molvae/tokens/<name>/) — the training home — and are mirrored here.

Everything shares one tokenizer (raw/tokenizer.json, vocab 1458; cls=1, sep=2, pad=3).

Raw sources (raw/)

Uniform schema per parquet: sequence (SELFIES string) + tokenized_sequence (int16, [cls, …ids, sep, pad…]). Highlights:

source	rows	notes
gdb/gdb13	977M	exhaustive ≤13-heavy-atom enumeration — dense strained/spiro/bridged rings
gdb/GDB-13s, gdb/gdb11, gdb/GDB4c3D, gdb/GDBMedChem, gdb/GDB17…	6.6M–99M	GDB variants (strained/ring-rich, small)
mcule_full/…	42M	purchasable; long molecules (median 46, to 154 tok)
emolecules/ordersc/…	36M	purchasable; long + diverse (to 168 tok)
Kaggle/chembl_22_clean…	1.6M	drug-like (short)
zinc22-2d-all/…	~1.9B	ZINC22 SELFIES by heavy-atom band H04–H49 (source of the zinc-tw2b-v2 corpus)

Mixes (mixes/)

See mixes/README.md for the catalog, composition tables, and the exact rebuild command. Builder: mixes/mix_dataset.py (from the molvae repo).

mix	mols	on S3	what
zinc-tw2b-v2	1.855B	…/tokens/zinc-tw2b-v2	the ZINC-only base corpus (H04–H49)
mix-phase1-40M	40M	…/tokens/mix-phase1-40M	v1 pilot: ZINC-heavy (40%) + GDB4c3D/GDBMedChem/ChEMBL
mix-v2-40M	40M	…/tokens/mix-v2-40M	v2 pilot: strained-heavy (GDB) + long (mcule/eMol)
mix-v2-1B	1B	…/tokens/mix-v2-1B	v2 full: the strained-rich pretrain mix

Held-out

The ~132 real mechanophores are a held-out reconstruction test set (not in any training mix; every build dedups against them). They live on S3 (…/eval/mechano_test.jsonl), not here.