ReviewAnalytical and bioanalytical chemistry2025
Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.
Review in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Machine Learning-Enhanced LC-HRMS Workflows for Suspect and Nontargeted Screening of Pesticide Residues in Fruits and Vegetables.Analytical chemistry · 2026Article
- Comprehensive Liquid Chromatography-Mass Spectrometry-based Metabolite Annotation Elucidates the Chemical Diversity of an Endangered Plant Nardostachys jatamansi.Journal of separation science · 2026Article
- Review
- From Target-Nontarget to Nontarget Screening: A Review on Screening Methods for Organic Pollutants Based on High-Resolution Mass Spectrometry.Environment & health (Washington, D.C.) · 2026Review
- Measurable Feature Prediction for Estimating Chemical Space Coverage in LC-ESI-HRMS Nontargeted Analysis.Analytical chemistry · 2026Article
- Molecular networking, conformal predictions and revised fingerprint-based models for discovering endocrine disruptors in mixtures.Analytical and bioanalytical chemistry · 2026Article
- Eco-Friendly Sample Preparation Trends for Exogenous Toxic Organic Compounds in Food: A Sustainable Perspective for LC-MS Analysis.Foods (Basel, Switzerland) · 2026Review
- Non-Targeted Analysis Workflow of Endocrine-Disrupting Chemicals in Ovarian Follicular Fluid: Identification of Parabens by Diagnostic Fragmentation Evidence and Additional Contaminants via Mass Spectral Library Matching.ACS measurement science au · 2025Article
- Trapped Ion Mobility Improves Annotation Accuracy in LC-HRMS Screening Applications for Exposomics.Analytical chemistry · 2025Article
- Large-scale generation of in silico based spectral libraries to annotate dark chemical space features in non-target analysis.Analytical and bioanalytical chemistry · 2025Article
- Lactic acid fermentation ofFood chemistry: X · 2025Article
- Do experimental projection methods outcompete retention time prediction models in non-target screening? A case study on LC/HRMS interlaboratory comparison data.The Analyst · 2025Article
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.
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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.