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Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening
ACS central science, v 9(2), pp 307-317
22 Feb 2023
PMID: 36844498
Abstract
Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors’ hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC–DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA’s data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA’s peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.
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Details
- Title
- Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening
- Creators
- Christian P. Haas - Massachusetts Institute of TechnologyMaximilian Lübbesmeyer - Massachusetts Institute of TechnologyEdward H. Jin - Massachusetts Institute of TechnologyMatthew A. McDonald - Massachusetts Institute of TechnologyBrent A. Koscher - Massachusetts Institute of TechnologyNicolas Guimond - Bayer (Germany)Laura Di Rocco - Bayer AG, Pharmaceuticals DivisionHenning Kayser - Bayer (Germany)Samuel Leweke - Bayer (Germany)Sebastian Niedenführ - Bayer (Germany)Rachel Nicholls - Bayer (Germany)Emily Greeves - Bayer (Germany)David M. Barber - Bayer (Germany)Julius Hillenbrand - Bayer (Germany)Giulio Volpin - Bayer (Germany)Klavs F. Jensen - Massachusetts Institute of Technology
- Publication Details
- ACS central science, v 9(2), pp 307-317
- Publisher
- American Chemical Society
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000963947400001
- Scopus ID
- 2-s2.0-85148105459
- Other Identifier
- 991021958009204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary