Spec2Vec

Spec2Vec applies a Word2Vec-derived machine learning approach to MS/MS spectra to compute spectral similarity scores that reflect structural relationships between molecules.

Key Features:

• Word2Vec adaptation: Adapts the Word2Vec algorithm from natural language processing to analyze relationships between mass fragments and neutral losses in tandem mass spectrometry (MS/MS) spectra.
• Innovative similarity scoring: Produces spectral similarity scores that capture fragmental relationships and address limitations of traditional cosine-based scores.
• Spectral embeddings: Generates abstract spectral embeddings that encapsulate intricate relationships among mass fragments and neutral losses for similarity evaluation.
• Correlation with structural similarity: Evaluation on GNPS MS/MS libraries containing nearly 13,000 unique molecules demonstrated superior correlation with true structural similarity compared to cosine-based scores.
• Computational efficiency: Enables computationally scalable searches for structural analogues within large databases in a matter of seconds.

Scientific Applications:

• Library Matching: Improves the accuracy and reliability of library matching by providing more precise spectral similarity scores.
• Molecular Networking: Enhances molecular networking capabilities for better organization and interpretation of complex spectral datasets.
• High-throughput metabolomics: Supports high-throughput metabolomics analyses via rapid similarity scoring and analogue searches.

Methodology:

Spec2Vec adapts the Word2Vec algorithm to analyze mass fragments and neutral losses in MS/MS spectra, learns fragmental relationships from large spectral datasets, generates spectral embeddings, and benchmarks similarity performance against GNPS MS/MS libraries (≈13,000 unique molecules) with comparisons to cosine-based scores.