Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure...

Area of Science:

• Computational chemistry
• cheminformatics
• spectroscopy

Background:

• Mass spectrometry is crucial for small molecule structure elucidation.
• Current software tools struggle with identifying many tandem mass spectrometry (MS/MS) spectra.
• Growing public MS/MS spectral data presents opportunities for deep learning (DL) solutions.

Purpose of the Study:

• To conceptualize and design novel DL architectures for automated small molecule structure elucidation from MS/MS data.
• To address limitations of current two-step DL approaches, including computational complexity and information loss.
• To improve substructure coverage and class imbalance issues inherent in molecular fingerprint prediction.

Main Methods:

• Proposed multitask learning to reduce classifier complexity by grouping related molecular descriptors.
• Introduced feature engineering to extract condensed, higher-order information from spectra and structure data.
• Incorporated spectral encoding with subtrees and patterns, and structural encoding with graph convolutional networks for joint embedding.

Main Results:

• Hypothetical DL architectures designed to enable simultaneous spectral library and molecular database searching.
• Feature engineering aims to capture peak interactions and molecular connectivity for richer data representation.
• Multitask learning is suggested for enhanced performance with fewer classifiers.

Conclusions:

• Deep learning frameworks hold potential for complete or partial small molecule structure prediction from MS/MS spectra.
• Sufficient training data, adapted architectures, and computational power are key for DL model success.
• Rigorous evaluation of DL frameworks against classical machine learning methods is essential for assessing performance and generalizability.