MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Area of Science:

• Natural Product Chemistry
• Drug Discovery
• Analytical Chemistry
• Bioinformatics

Background:

• Natural products (NPs) were historically vital for small-molecule drug discovery.
• Interest in NPs waned due to technical bottlenecks and the rise of alternative methods.
• Compound dereplication from complex natural mixtures is a significant hurdle.

Purpose of the Study:

• To review the evolution and application of tandem mass spectrometry (MS/MS)-based molecular networking (MN) for natural product research.
• To provide an overview of classical MN (CLMN) and its subsequent advanced methodologies.
• To facilitate further research and application of MN techniques in drug discovery.

Main Methods:

• Review of molecular networking (MN) techniques, including classical MN (CLMN).
• Discussion of advanced MN methods: feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based MN (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN).
• Analysis of basic principles, workflows, and application examples of these MN methods.

Main Results:

• MS/MS-based molecular networking (MN) emerged in 2012 as an efficient approach to avoid rediscovering known compounds.
• Several advanced MN methods have been developed over the past decade, building upon the classical framework.
• These methods leverage omics, advanced instrumentation, and artificial intelligence for analyzing complex natural mixtures.

Conclusions:

• Molecular networking (MN) significantly enhances the efficiency of natural product drug discovery by addressing dereplication challenges.
• The diverse MN methodologies offer powerful tools for navigating complex natural product landscapes.
• Continued research and application of MN techniques are crucial for unlocking the potential of natural products in medicine.