Xu-AI4Science/fragscape-data

FragScape data

Derived data from FragScape, a study of formula -> molecular structure generation and of same-formula candidate ranking from MS/MS spectra. Code: https://github.com/GainGod-Xu/fragscape.

The project's measurements are archived here together with the inputs needed to reproduce them. Raw third-party data is not mirrored; see Not included below.

What is here

path	size	what it is
mols/formula_corpus.tsv.gz	3.9 GB	the 142M-molecule corpus: SMILES \t formula \t InChIKey14, one molecule per line, no header. Built from MassSpecGym's 50M pretraining dump plus its 118M PubChem retrieval pool, multi-component entries dropped
mols/holdout_keys.txt	367 KB	InChIKey14s held out of the corpus. Every number reported on held-out molecules uses this file
mols/recovery_eval.tsv	1.5 MB	the 2,470 molecules of the recovery evaluation
decoys/	1.4 GB	same-formula candidate pools built from the corpus: 8,833 heavy-atom formulas, 19,067,059 candidates, median 1,885 per formula, capped at 4,096. cands.smi / cands.keys / cands.formula are line-aligned; cands_index.json gives each formula's offset
cands/	136 MB	the smaller pool used before decoys/ replaced it (median 2 candidates, 44% singletons - kept only so earlier numbers remain checkable)
results/	269 MB	every evaluation output, one JSON per run, plus the MSn scoring sweeps
logs/	68 MB	Slurm stdout/stderr for the runs behind those JSONs, including the RESULT lines

Model weights are in the companion repo Xu-AI4Science/fragscape.

The three measurements worth knowing about

These are the reasons the data is shaped the way it is. All ranks break ties against the truth, and every scorer has a shuffled-input control (each spectrum paired with a different molecule's) because a scorer that ignores its input otherwise looks fine.

1. Fragment enumeration beats every published learned ranker on same-formula pools, with no training at all. On 400 held-out MassSpecGymMSn molecules, official candidate pools (median 245), MS2 only:

scorer	top-1	top-5	top-10	top-20	median rank
fragment enumeration, real spectra	22.25% [18.45, 26.58]	57.75%	72.75%	84.50%	4
fragment enumeration, shuffled spectra	1.25%	-	9.00%	-	88

The score is explained intensity: enumerate every fragment up to 2 bond breaks, match each peak within max(10 ppm, 0.005 Da) allowing +-2 H, sum the matched intensity. For reference, the best published number on the equivalent formula-restricted setting of the MassSpecGym benchmark (256 same-formula candidates) is MIST's 9.57% top-1. Those are different test molecules, so this is not a like-for-like claim - the matched comparison on MassSpecGym's own test fold was never run.

2. The learned contrastive ranker never read the spectrum. Two arms (frozen formula LM tower; trainable 5-layer GINE tower) reached 1.75-3.75% top-1, at or below the benchmark's random baseline of 3.06%. Blinding the spectrum changed top-1 by between -0.47 and +0.03 pp, i.e. not at all. The checkpoints are archived so this is checkable.

3. Coverage, not ranking, is the ceiling. Sampling the formula-conditioned prior 1,000 times covers the true molecule for 5-6% of spectra, and top-1 <= coverage is arithmetic. Scaling the prior from 25M to 110M parameters improved held-out loss 0.3669 -> 0.3366 and moved recovery@1000 from 3.60% to 5.08%, but left recovery@1 unchanged at 0.04% (1 molecule in 2,440). The corpus axis is flat: 5.5M / 13.2M / 44.0M / 132.1M molecules give 3.32 / 3.81 / 3.36 / 3.60% at 1,000 draws. More draws is the only axis that moves, and it is sublinear - 1.0 / 4.0 / 13.0% at 100 / 1k / 10k.

Not included

Third-party data is left at its source rather than mirrored:

• MassSpecGym and MassSpecGymMSn (spectra, splits, official retrieval candidate lists): HuggingFace dataset roman-bushuiev/MassSpecGym. The two raw dumps the corpus was built from are data/molecules/MassSpecGym1.5_pretraining_molecules_50M_tani070.txt and data/molecules/candidate_pools/MassSpecGym_retrieval_molecules_pubchem_118M.tsv.
• The tokenized corpus cache (13 GB) is regenerable from mols/formula_corpus.tsv.gz with scripts/sbatch_tokenize_corpus.sh.

Citing the benchmark

Bushuiev et al., MassSpecGym: A benchmark for the discovery and identification of molecules, Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Datasets and Benchmarks Track. The 9.57% and 3.06% figures above are from its Table 5, bonus chemical formulae setting.