AIMC Topic: Tandem Mass Spectrometry
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Identification, synthesis, and characterization of an unprecedented N-(2-carboxyethyl) adduct impurity in an injectable ganirelix formulation.
Journal of peptide science : an official publication of the European Peptide Society
Dec 22, 2023
Ganirelix, a peptide-based drug used to treat female infertility, has been in high market demand, which attracted generic formulation. A hitherto unknown impurity of ganirelix was observed in our formulation process, which reached ~0.3% in 6 months a...
Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.
Analytical chemistry
Dec 4, 2023
The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying th...
Variability analysis of LC-MS experimental factors and their impact on machine learning.
GigaScience
Nov 20, 2023
BACKGROUND: Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescori...
MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.
Journal of chemical information and modeling
Sep 19, 2023
Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends o...
MSBooster: improving peptide identification rates using deep learning-based features.
Nature communications
Jul 27, 2023
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFrag...
DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.
Analytical chemistry
Jul 17, 2023
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the p...
Microextraction by packed sorbent of N-nitrosamines from Losartan tablets using a high-throughput robot platform followed by liquid chromatography-tandem mass spectrometry.
Journal of separation science
Jul 3, 2023
The development of a fast, cost-effective, and efficient microextraction by packed sorbent setup was achieved by combining affordable laboratory-repackable devices of microextraction with a high-throughput cartesian robot. This setup was evaluated fo...
Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms.
Analytical and bioanalytical chemistry
Apr 29, 2023
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexp...
DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.
Analytical chemistry
Mar 12, 2023
In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of metho...
An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2023
Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrat...
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