Introduction

The NGM 3 Pro (Next Generation Metabolomics) is designed to maximize analytical accuracy and reliability. Built on an proprietary in-house library of over 20,000 metabolite standards and powered by the Orbitrap Astral mass spectrometer, it integrates a stringent four-core algorithm framework, automated sample preprocessing and end-to-end quality control workflow to substantially enhance data precision. In addition, by leveraging the MetDNA3 metabolic network database, NGM 3 Pro enables the discovery of previously undetected metabolic "dark matter", helping researchers overcome critical bottlenecks in scientific investigation.

Technical Advantages

◉  Supported by a proprietary in-house library consisting of 20,000+ metabolite standards

◉  Powered by the patented MetDNA3 algorithm

◉  Total metabolite identifications exceeding 12,000(average >9,000); Level 1 identifications exceeding 2,200(average >2,000)

◉  Customizable detection with prioritized analysis of user-defined metabolites lists

◉  Capability to discover previously unknown metabolites

Samples requirements

Platform

Orbitrap Astral,Thermo

Applications

Applicable across clinical medicine, traditional Chinese medicine, pharmaceuticals, food science, environmental exposure studies, livestock and aquaculture. Supports research in disease mechanisms, drug efficacy, food metabolism, environmental toxicology, and related fields.

Publication

Title: Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics

Journal：Nature Communications

Impact Factor: 15.7

Background:

LC-MS-based untargeted metabolomics enables detection of both known and unknown metabolites in biological samples. However, reliable annotation of unknown metabolites remains as a central challenge in untargeted metabolomics.

Key Findings: Within MetDNA3, the authors developed a two-layer interactive network topology to enhance metabolite annotation. Recursive annotation achieved through cross-network interactions markedly improved identification efficiency, coverage and accuracy, providing strong technical support for research for metabolomics as well as broader life-science and biomedical applications.

Results: Across multiple biological sample datasets, MetDNA3 successfully annotated over 1,600 level 1 metabolites, and annotated more than 12,000 metabolites in total through using network-topology-based strategies. Building on this framework, the dual-network-driven propagation and iterative annotation approach enabled the discovery and validation of two previously unreported metabolites absent from existing human metabolome databases. The study further demonstrated that highly specific knowledge networks are critical for improving both annotation accuracy and propagation performance within network-based identification systems.

Fig.Improved coverage and correct rate of metabolite annotation.