Molecular database loading and parsing

molecular-database-loading-and-parsing

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Summary

Load and parse serialized molecular structure databases (e.g., HMDB compounds in pickle format) into memory, then apply m/z range and MS level filters to extract a curated set of unique molecular formulas for chemical sampling in virtual metabolomics simulations.

When to use

When setting up a ViMMS chemical sampling environment and you need to restrict the chemical search space to a specific m/z range (e.g., 100–1000) and MS level (e.g., MS1 only) before generating virtual LC-MS/MS data. Use this skill before initializing a DatabaseFormulaSampler or equivalent chemical generator.

When NOT to use

• Database is already in memory and pre-filtered — skip loading and filtering steps and pass the formula set directly to the sampler.
• You need to preserve all formulas including adducts and isotopologues — this skill removes duplicates and MS level variants.
• Target m/z range extends outside the database's native coverage — verify database m/z distribution before setting thresholds.

Inputs

• Serialized molecule database file (pickle format, e.g., hmdb_compounds.p)
• m/z range parameters (min_mz, max_mz as floats)
• MS level filter criterion (ms_levels as integer)

Outputs

• Filtered set of unique molecular formulas (Python list or set)
• De-duplicated formula count (integer)
• Summary report documenting filter criteria and result count

How to apply

Load the HMDB compounds database (hmdb_compounds.p) using Python pickle deserialization. Apply a minimum and maximum m/z boundary filter (e.g., min_mz=100, max_mz=1000) to retain only molecular formulas within the instrument's operating range. Filter further to retain only MS level 1 compounds (ms_levels=1) to focus on parent ion-level chemistry. De-duplicate the filtered formula set to obtain a canonical list of unique molecular formulas. Count and record the total number of unique formulas in a summary report; this count (e.g., 73822 formulas for HMDB with m/z 100–1000) documents the effective search space for the simulation.

Related tools

• ViMMS (Simulation framework that consumes the filtered formula set via DatabaseFormulaSampler to generate virtual chemical samples for LC-MS/MS acquisition simulation) — https://github.com/glasgowcompbio/vimms
• Python pickle (Standard library module for deserializing the HMDB database from binary pickle format into Python objects)

Examples

import pickle
db = pickle.load(open('hmdb_compounds.p', 'rb'))
filtered = [c for c in db if 100 <= c.get('mz', 0) <= 1000 and c.get('ms_levels') == 1]
unique_formulas = list(set(c['formula'] for c in filtered))
print(f'Unique formulas: {len(unique_formulas)}')

Evaluation signals

• Pickle deserialization succeeds without corruption or version mismatch errors; database object structure matches expected schema (e.g., list of compound dicts with 'formula', 'mz', 'ms_levels' keys).
• m/z filter: verify min_mz ≤ all retained formulas' m/z ≤ max_mz; confirm count of out-of-range formulas are excluded.
• MS level filter: spot-check a random sample of retained formulas to confirm ms_levels == 1; verify count of filtered-out multi-level entries.
• De-duplication check: compare filtered count to count after applying set() on formula strings; counts must be equal.
• Reported unique formula count (e.g., 73822 for HMDB m/z 100–1000) is reproducible across multiple runs with identical parameters; log the count and filter parameters for audit trail.

Limitations

• Database file format (pickle) is Python-specific and not human-readable; validation requires unpickling. If file is corrupted or from an incompatible Python/library version, deserialization will fail.
• m/z filtering assumes formula molecular weight correlates linearly with m/z; adducts, charge states, and post-ionization modifications are not accounted for in this step.
• De-duplication is string-based (e.g., 'C6H12O6' == 'C6H12O6'); isomers with identical molecular formula are collapsed into one, potentially losing chemical diversity.
• MS level filtering to 1 only excludes doubly-charged, multiply-ionized, and fragmented species; may not suit high-resolution or ion-mobility workflows.

Evidence

• [other] Load the HMDB compounds database (hmdb_compounds.p) using Python pickle: "1. Load the HMDB compounds database (hmdb_compounds.p) using Python pickle."
• [other] Apply m/z range filter (min_mz=100, max_mz=1000) to retain only formulas within the specified range: "2. Apply m/z range filter (min_mz=100, max_mz=1000) to retain only formulas within the specified range."
• [other] Filter to retain only MS level 1 compounds by selecting those with ms_levels=1: "3. Filter to retain only MS level 1 compounds by selecting those with ms_levels=1."
• [other] De-duplicate the filtered formula set to obtain unique molecular formula entries: "4. De-duplicate the filtered formula set to obtain unique molecular formula entries."
• [other] When the DatabaseFormulaSampler filters the HMDB database for formulas with m/z between 100 and 1000, it yields 73822 unique molecular formulas: "When the DatabaseFormulaSampler filters the HMDB database for formulas with m/z between 100 and 1000, it yields 73822 unique molecular formulas."
• [other] Chemicals can be sampled from databases such as HMDB or from specific distributions: "Chemicals can be sampled from databases such as HMDB or from specific distributions"