Pseudo Data Pair Construction

pseudo-data-pair-construction

Summary

Generation of large-scale pseudo SMILES-spectrum pairs by invoking CFM-ID on raw SMILES lists to create synthetic training data for molecular structure inference models. This skill bridges limited real spectroscopic datasets by producing 30k+ paired examples suitable for deep learning on mass spectrometry-to-structure tasks.

When to use

When you have a raw list of SMILES strings but lack sufficient real mass spectrometry reference data (typically <300 spectra) to train a generative model for unknown chemical identification. Apply this skill to amplify training set size via in silico fragmentation, particularly for underrepresented molecular classes (e.g., PFASs, lipids) in public spectroscopy databases.

When NOT to use

• You already have >300 real experimental mass spectra for your target molecular class — use real data first to avoid synthetic bias.
• Your SMILES list is incomplete, malformed, or contains non-organic structures that CFM-ID cannot process reliably.
• Your model or downstream task requires retention of isotope patterns or instrument-specific artifacts that in silico fragmentation does not simulate.

Inputs

• raw SMILES list (text file, one SMILES per line or CSV column)
• CFM-ID executable or API endpoint

Outputs

• aggregated pseudo SMILES-spectrum pair dataset (≥30,000 pairs)
• training data file (format: paired SMILES and spectrum representation)

How to apply

Load a SMILES string list into memory and invoke CFM-ID for each SMILES to generate in silico mass spectra with simulated fragmentation patterns. Pair each input SMILES with its CFM-ID-generated spectrum object to form (SMILES, spectrum) tuples. Aggregate all pairs into a single standardized training dataset file, ensuring the output contains ≥30,000 pairs. Each pair must preserve both a valid SMILES string representation and a corresponding spectrum (e.g., m/z intensity array or CFM-ID native format). Validate cardinality (count = expected size), completeness (no null SMILES or spectrum fields), and format consistency before downstream model training.

Related tools

• cfmid (Invoked per SMILES to generate in silico mass spectra with fragmentation patterns)
• Python 3.7 (Orchestration: SMILES list I/O, CFM-ID invocation loop, pair aggregation, validation)
• MSGO (Downstream consumer of generated pseudo SMILES-spectrum pairs for model training) — https://github.com/aaronma2020/MSGO

Examples

# Pseudocode workflow (Python 3.7):
# smiles_list = load_smiles('data/raw_smiles.txt')
# pairs = []
# for smiles in smiles_list:
#     spectrum = invoke_cfmid(smiles)
#     pairs.append((smiles, spectrum))
# save_pairs(pairs, 'data/pseudo_smiles_spectrum_pairs.pkl')
# assert len(pairs) >= 30000, f'Expected >=30k pairs, got {len(pairs)}'

Evaluation signals

• Output file contains exactly ≥30,000 (SMILES, spectrum) pairs with no duplicates or null entries.
• Each SMILES string parses successfully (e.g., via RDKit) and is chemically valid.
• Each spectrum contains expected mass/charge and intensity fields with numeric values in expected ranges (e.g., m/z > 0, intensity ≥ 0).
• Spot-check: manually verify 10–20 SMILES-spectrum pairs by running SMILES through CFM-ID independently and confirming fragment peaks match the paired spectrum.
• Downstream MSGO training converges (loss decreases, validation metrics improve) on the generated dataset with comparable learning curves to published baselines.

Limitations

• CFM-ID fragmentation patterns may not capture all ionization modes, adducts, or high-energy collision behavior of real instruments.
• Pseudo pairs lack the chemical noise, detector artifacts, and retention-time information present in authentic LC–QTOF data; model trained on pseudo data may overfit to synthetic patterns.
• SMILES list must be curated to avoid invalid or stereochemically ambiguous entries that cause CFM-ID errors or silent failures.
• Generation speed depends on CFM-ID computational cost per SMILES; 30k+ pairs may require hours to days on commodity hardware.

Evidence

• [other] Training data for MSGO consists of 30k+ pseudo SMILES-spectrum pairs generated by CFM-ID from raw SMILES lists.: "Training data for MSGO consists of 30k+ pseudo SMILES-spectrum pairs generated by CFM-ID from raw SMILES lists."
• [other] For each SMILES string, invoke CFM-ID to generate in silico mass spectra with fragmentation patterns.: "For each SMILES string, invoke CFM-ID to generate in silico mass spectra with fragmentation patterns."
• [other] Pair each input SMILES with its corresponding CFM-ID-generated spectrum to create pseudo SMILES-spectrum pairs.: "Pair each input SMILES with its corresponding CFM-ID-generated spectrum to create pseudo SMILES-spectrum pairs."
• [other] Aggregate all pairs into a single training dataset file, ensuring at least 30,000+ pairs are produced.: "Aggregate all pairs into a single training dataset file, ensuring at least 30,000+ pairs are produced."
• [readme] For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid: "For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid"