Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

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.