ArticleNature communications2023
Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
22 citing papers in PubMed.
- Beyond empirical models: Discovering constitutive laws in solids with graph-based equation discovery.Science advances · 2026Article
- Enantiomer-Resolved Biological Profiling and Chiral Developability Assessment of Novel Naphthylethyl Thioureas.Pharmaceutics · 2026Article
- Chiral atom-centered scalar triple products for predicting enantiomer elution order in HPLC.Journal of computer-aided molecular design · 2026Article
- A Chirality-Converted Bacteriolytic Dodecapeptide Regulates Vibrio-Induced Polymicrobial Infection and Ameliorates Invasion-Associated Gut Microbiota Disequilibrium.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.Precision chemistry · 2026Article
- A multiplatform chromatography-mass spectrometry dataset for targeted and suspect screening of pollutants.Scientific data · 2026Article
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Toward AI-Assisted Greener Chiral HPLC: Predicting Efficient Enantioseparation-Mobile Phase (EES-MP) Profiles for MP Selection─A Lux Cellulose-1 Case Study.Analytical chemistry · 2026Article
- Cyclodextrins as Chiral Selectors in Capillary Electromigration Techniques.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Enantioselective protein affinity selection mass spectrometry (E-ASMS).Nature communications · 2025Article
- Computed ECD spectral data for over 10,000 chiral organic small molecules.Scientific data · 2025Article
- Evaluation of chirality descriptors derived from SMILES heteroencoders.Journal of cheminformatics · 2025Article
- Quantum Chemistry Based Simulation of Enantioseparation on Cyclodextrin- and Polysaccharide-Based Chiral Stationary Phases.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Neuron signal attenuation activation mechanism for deep learning.Patterns (New York, N.Y.) · 2025Article
- Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds.Research (Washington, D.C.) · 2025Article
- Application of artificial intelligence to quantitative structure-retention relationship calculations in chromatography.Journal of pharmaceutical analysis · 2025Review
- Insights into predicting small molecule retention times in liquid chromatography using deep learning.Journal of cheminformatics · 2024Review
- Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems?Analytical chemistry · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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 settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.
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Registered trials
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.