NORMAN guidance on suspect and non-target screening in environmental monitoring
Increasing production and use of chemicals and awareness of their impact on ecosystems
and humans has led to large interest for broadening the knowledge on the chemical status …
and humans has led to large interest for broadening the knowledge on the chemical status …
Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics
Metabolomics deals with the large-scale analysis of metabolites, belonging to numerous
compound classes and showing an extremely high chemical diversity and complexity …
compound classes and showing an extremely high chemical diversity and complexity …
Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network
The enantioseparation of chiral molecules is a crucial and challenging task in the field of
experimental chemistry, often requiring extensive trial and error with different experimental …
experimental chemistry, often requiring extensive trial and error with different experimental …
RepoRT: a comprehensive repository for small molecule retention times
Liquid chromatography (LC) is fre-quently used to separate metabolites and other small
molecules. Retention time is the time required for a particular molecule to pass through the …
molecules. Retention time is the time required for a particular molecule to pass through the …
Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data
Structural annotation of small molecules in biological samples remains a key bottleneck in
untargeted metabolomics, despite rapid progress in predictive methods and tools during the …
untargeted metabolomics, despite rapid progress in predictive methods and tools during the …
Retention time dataset for heterogeneous molecules in reversed–phase liquid chromatography
Y Zhang, F Liu, XQ Li, Y Gao, KC Li, QH Zhang - Scientific Data, 2024 - nature.com
Quantitative structure–property relationships have been extensively studied in the field of
predicting retention times in liquid chromatography (LC). However, making transferable …
predicting retention times in liquid chromatography (LC). However, making transferable …
Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network
Graph neural networks (GNNs) have shown remarkable performance in predicting the
retention time (RT) for small molecules. However, the training data set for a particular target …
retention time (RT) for small molecules. However, the training data set for a particular target …
[HTML][HTML] Identification of nutritional biomarkers through highly sensitive and chemoselective metabolomics
The importance of a healthy diet for humans is known for decades. The elucidation of key
molecules responsible for the beneficial and adverse dietary effects is slowly develo** as …
molecules responsible for the beneficial and adverse dietary effects is slowly develo** as …
Insights into predicting small molecule retention times in liquid chromatography using deep learning
In untargeted metabolomics, structures of small molecules are annotated using liquid
chromatography-mass spectrometry by leveraging information from the molecular retention …
chromatography-mass spectrometry by leveraging information from the molecular retention …
Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning
In chemistry, empirical paradigms prevail, especially within the realm of chromatography,
where the selection of separation conditions frequently relies on the chemist's experience …
where the selection of separation conditions frequently relies on the chemist's experience …