AIMC Topic: Chromatography, Reverse-Phase
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Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.
Journal of chemical information and modeling
Mar 28, 2025
Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input ...
Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.
Journal of chromatography. A
Jul 9, 2024
Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...
A perspective on the use of deep deterministic policy gradient reinforcement learning for retention time modeling in reversed-phase liquid chromatography.
Journal of chromatography. A
Dec 10, 2023
Artificial intelligence and machine learning techniques are increasingly used for different tasks related to method development in liquid chromatography. In this study, the possibilities of a reinforcement learning algorithm, more specifically a deep...
Convolutional neural network for automated peak detection in reversed-phase liquid chromatography.
Journal of chromatography. A
Mar 31, 2022
Although commercially available software provides options for automatic peak detection, visual inspection and manual corrections are often needed. Peak detection algorithms commonly employed require carefully written rules and thresholds to increase ...
Deep learning for retention time prediction in reversed-phase liquid chromatography.
Journal of chromatography. A
Dec 30, 2021
Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. ...
Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data.
Analytical chemistry
Nov 15, 2021
Machine learning is a popular technique to predict the retention times of molecules based on descriptors. Descriptors and associated labels (e.g., retention times) of a set of molecules can be used to train a machine learning algorithm. However, desc...
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.
Nature communications
May 28, 2021
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...
Application of Design of Experiments® Approach-Driven Artificial Intelligence and Machine Learning for Systematic Optimization of Reverse Phase High Performance Liquid Chromatography Method to Analyze Simultaneously Two Drugs (Cyclosporin A and Etodolac) in Solution, Human Plasma, Nanocapsules, and Emulsions.
AAPS PharmSciTech
May 13, 2021
The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target p...
The METLIN small molecule dataset for machine learning-based retention time prediction.
Nature communications
Dec 20, 2019
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retent...
Micelle-dominated distribution strategy for non-matrix matched calibration without an internal standard: "Extract-and-shoot" approach for analyzing hydrophilic targets in blood and cell samples.
Analytica chimica acta
Dec 17, 2019
The analysis of trace hydrophilic targets in complex aqueous-rich matrices is considerably challenging, generally requiring matrix-matched calibration, internal standard, or time-and-labor-intensive sample preparation. To address this analytical bott...
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