ArticleComputational and structural biotechnology journal2024
Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comprehensive Liquid Chromatography-Mass Spectrometry-based Metabolite Annotation Elucidates the Chemical Diversity of an Endangered Plant Nardostachys jatamansi.Journal of separation science · 2026Article
- Non-Targeted and Targeted Screening of Organic Contaminants in Honeybees' Death Incidents in Greece: A Story Beyond Pesticides.Journal of xenobiotics · 2026Article
- Drug pair-derived synergistic therapy of flavonoids luteolin and astragaloside IV promotes neural repair following spinal cord injury via antioxidant and neuroprotective effects.Precision clinical medicine · 2026Article
- Breaking the boundaries of affinity selection-mass spectrometry: From ligand screening to target-ligand interaction insights.Journal of pharmaceutical analysis · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Mass Spectrometry as a Tool for Metabolomics in Cancer.Advances in experimental medicine and biology · 2026Review
- Article
- Large-scale generation of in silico based spectral libraries to annotate dark chemical space features in non-target analysis.Analytical and bioanalytical chemistry · 2025Article
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
- HerbComb: An integrated database for the discovery of novel combinational therapies from herbal medicines.Computational and structural biotechnology journal · 2025Article
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Authors and funding
14 authors.
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Abstract
Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is a potent analytical technique utilized for identifying natural products from complex sources. However, due to the structural diversity, annotating LC-MS/MS data of natural products efficiently remains challenging, hindering the discovery process of novel active structures. Here, we introduce MassKG, an algorithm that combines a knowledge-based fragmentation strategy and a deep learning-based molecule generation model to aid in rapid dereplication and the discovery of novel NP structures. Specifically, MassKG has compiled 407,720 known NP structures and, based on this, generated 266,353 new structures using chemical language models for the discovery of potential novel compounds. Furthermore, MassKG demonstrates exceptional performance in spectra annotation compared to state-of-the-art algorithms. To enhance usability, MassKG has been implemented as a web server for annotating tandem mass spectral data (MS/MS, MS2) with a user-friendly interface, automatic reporting, and fragment tree visualization. Lastly, the interpretive capability of MassKG is comprehensively validated through composition analysis and MS annotation of
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