ArticleNature methods2021
Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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Who cites it
103 citing papers in PubMed, 204 citations in OpenAlex.
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- PyCompound: An Open-Source Python Package for Spectral-Library Matching in Mass Spectrometry-Based Metabolomics.Metabolites · 2026Article
- Using MetaboAnalyst 6.0 for exposomics data analysis-from LC-MS2 spectra processing to dose-response modeling and causal inference.Nature protocols · 2026Review
- PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites.Journal of the American Society for Mass Spectrometry · 2026Article
- Benchmarking MS/MS Featurization Strategies for Machine Learning-Driven Metabolite Structure Annotation.Journal of the American Society for Mass Spectrometry · 2026Article
- Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by Transfer Learning-Based Neural Network.Environmental science & technology · 2026Article
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- Practical Workflow for Building Local Mass Spectral Libraries for Untargeted Metabolomics.Metabolites · 2026Article
- MutAIverse: an AI-powered, mechanism-backed platform for discovering novel DNA adducts and their precursor genotoxins.Journal of cheminformatics · 2026Article
- Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.Nature communications · 2026Article
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics.Nature communications · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries.Journal of cheminformatics · 2026Article
- Perceiving Unpredictability for New Energy Power and Electricity Consumption Forecasting.Entropy (Basel, Switzerland) · 2026Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.