ArticleChemical science2024
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- High-Accuracy Prediction of Chunmee Tea Grade via DeepSpectra Model and Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2026Article
- DyeDactic workflow to predict halochromism of biosynthetic colourants.Communications chemistry · 2026Article
- Article
- Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates.Journal of the American Chemical Society · 2025Article
- Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules.ACS central science · 2025Article
- Direct (LC-)MS Identification of Regioisomers from C-H Functionalization by Partial Isotopic Labeling.ACS central science · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.
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Registered trials
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