Machine learning-assisted structure annotation of natural products based on MS and NMR data

Area of Science:

• Chemistry
• Computational Chemistry
• Biochemistry

Background:

• Natural products (NPs) are crucial in drug discovery, but their structural elucidation is challenging.
• Machine learning (ML) offers powerful tools for analyzing complex chemical data.
• Recent advancements have significantly improved ML's application in NP structure determination.

Purpose of the Study:

• To review recent progress in ML-assisted analysis of mass spectrometry (MS) and nuclear magnetic resonance (NMR) data for NP structure elucidation.
• To summarize ML applications in both library-dependent and library-independent MS/MS analyses.
• To explore the role of ML in NMR-based structural studies of NPs.

Main Methods:

• Review of ML algorithms applied to MS/MS data, including similarity calculations, fragment prediction, and molecular fingerprinting.
• Analysis of ML techniques for MS/MS structural annotation without prior library matching.
• Examination of ML applications in NMR data analysis: prediction, functional group identification, structural categorization, and quantum chemical calculations.

Main Results:

• ML significantly enhances the accuracy and efficiency of NP structure elucidation from MS/MS and NMR data.
• ML algorithms can predict MS/MS fragments and identify molecular fingerprints, aiding library matching.
• ML facilitates structural annotation without libraries and aids in NMR-based studies through prediction and categorization.

Conclusions:

• ML is a transformative tool for natural product structure elucidation, improving speed and accuracy.
• Challenges remain in developing robust ML models and integrating diverse data types.
• Future trends point towards more sophisticated ML algorithms and broader applications in NP research.