ArticleAnalytical and bioanalytical chemistry2021
Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis.
Article in Analytical and bioanalytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Quantitative method comparison in non-targeted analysis using cascade classification.Analytical and bioanalytical chemistry · 2026Article
- Hydrogen sulfide alleviates thiocyanate stress in rice seedlings via tissue-specific carbon and nitrogen metabolic reprogramming.Frontiers in plant science · 2026Article
- Reference library for suspect screening of environmental toxicants using ion mobility spectrometry-mass spectrometry.Communications chemistry · 2025Article
- CombinedJournal of chemical information and modeling · 2025Article
- Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories.Environmental science & technology · 2025Article
- Filling the Gaps in PFAS Detection: Integrating GC-MS Non-Targeted Analysis for Comprehensive Environmental Monitoring and Exposure Assessment.Environmental science & technology letters · 2025Article
- Analytical Quality Evaluation of the Tox21 Compound Library.Chemical research in toxicology · 2025Review
- Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice.Environmental science & technology · 2025Article
- Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.Analytical and bioanalytical chemistry · 2025Review
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis.Analytical and bioanalytical chemistry · 2024Article
- Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry.Analytical chemistry · 2024Review
- Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification.Environmental science & technology · 2024Article
- Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency.Environment international · 2023Review
- Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set.Chemical research in toxicology · 2023Article
- Identifying xenobiotic metabolites withFrontiers in toxicology · 2023Article
- Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool.Analytical and bioanalytical chemistry · 2023Article
- Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.Journal of exposure science & environmental epidemiology · 2022Review
- First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry.Analytical chemistry · 2022Article
- A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern.Metabolites · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.
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