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Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
Chemical science (Cambridge), v 15(26), pp 10092-10100
03 Jul 2024
PMID: 38966367
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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Details
- Title
- Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
- Creators
- Matthew A McDonald - Massachusetts Institute of TechnologyBrent A Koscher - Massachusetts Institute of TechnologyRichard B Canty - Massachusetts Institute of TechnologyKlavs F Jensen - Massachusetts Institute of Technology
- Publication Details
- Chemical science (Cambridge), v 15(26), pp 10092-10100
- Publisher
- Royal Society of Chemistry
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:001234071500001
- Scopus ID
- 2-s2.0-85194759739
- Other Identifier
- 991021958008304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Chemistry, Multidisciplinary