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nico8771/moses_test_scaffolds

nico8771/moses_test_scaffolds — cleaned MOSES (test_scaffolds split) Each row is a molecule as canonical SMILES plus RDKit-recomputed targets. MOSES is neutral by construction, so molecules are featurized over 7 atom types with no formal charges, and aromatic bonds are kept as their own class (no kekulization) so the model learns aromaticity directly. Source: official MOSES test_scaffolds.csv.gz (molecularsets/moses). Code: https://github.com/Nico-Conti/flow-matching-molecules… See the full description on the dataset page: https://huggingface.co/datasets/nico8771/moses_test_scaffolds.

sourceHugging Faceotherupdated 3mo agoView on Hugging Face

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Dataset Card

nico8771/mosestestscaffolds — cleaned MOSES (test_scaffolds split)

Each row is a molecule as canonical SMILES plus RDKit-recomputed targets. MOSES is neutral by construction, so molecules are featurized over 7 atom types with no formal charges, and aromatic bonds are kept as their own class (no kekulization) so the model learns aromaticity directly.

Source: official MOSES test_scaffolds.csv.gz (molecularsets/moses). Code: <https://github.com/Nico-Conti/flow-matching-molecules> (dataset/).

Schema

column	type	description
`smiles`	string	canonical, single-fragment SMILES (post-sanitize)
`y`	list[float]	RDKit targets, columns = `logP`, `qed`, `SAS`

Pipeline

1.Parse with RDKit; unparseable dropped.
2.Standardize — remove stereochemistry, sanitize (MOSES is neutral; no Uncharger).
3.Featurize over atom vocab (C, N, S, O, F, Cl, Br); atoms outside the vocab dropped.
4.Round-trip check — smiles -> (X, E) -> mol -> smiles, aromatic bonds preserved.

Bonds use 5 classes (none / single / double / triple / aromatic). Targets (logP, qed, SAS) are recomputed from the sanitized SMILES with RDKit.

Drop / keep counts (this build)

outcome	count
`kept`	155,697
`kept_no_roundtrip`	20,528

Kept: 176,225 molecules.