Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

Area of Science:

• Chemical Synthesis
• Analytical Chemistry
• Data Science

Background:

• Automation in small molecule synthesis requires advanced chemical reaction analysis.
• High-performance liquid chromatography (HPLC) data is often inaccessible due to proprietary software, hindering automated workflows.
• A need exists for open-source tools to analyze chromatographic data for improved efficiency and data science applications.

Purpose of the Study:

• To present MOCCA, an open-source Python project for analyzing raw HPLC-Diode Array Detector (DAD) data.
• To provide automated peak deconvolution for complex chromatographic signals, including overlapped peaks.
• To demonstrate the utility of MOCCA in various chemical synthesis and optimization studies.

Main Methods:

• Development of the MOCCA Python package for HPLC-DAD data analysis.
• Implementation of an automated peak deconvolution algorithm.
• Validation through a simulation study and application in four diverse case studies.

Main Results:

• MOCCA successfully analyzes raw HPLC-DAD data, including automated deconvolution of overlapped peaks.
• Demonstrated applicability in reaction kinetics, closed-loop optimization, and high-throughput screening.
• Validated MOCCA's performance in a simulation study and real-world chemical synthesis scenarios.

Conclusions:

• MOCCA offers a powerful, open-source solution for analyzing HPLC-DAD data, overcoming proprietary software limitations.
• The tool facilitates automated workflows and data science applications in chemical synthesis.
• MOCCA is positioned to foster a community-driven approach to advancing chromatographic data analysis.