Automated LC-MS Analysis and Data Extraction for High-Throughput Chemistry

25 July 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


High-throughput experimentation for chemistry and chemical biology has emerged as a highly impactful technology, particularly when applied to Direct-to-Biology. Analysis of the rich datasets which come from this mode of experimentation continues to be the rate-limiting step to reaction optimisation and the submission of compounds for biological assay. We present PyParse, an automated, accurate and accessible program for data extraction from high-throughput chemistry and provide real-life examples of situations in which PyParse can provide dramatic improvements in the speed and accuracy of analysing plate data. This software package has been made available through GitHub repository under an open-source Apache 2.0 licence, to facilitate the widespread adoption of high-throughput chemistry and enable the creation of standardised chemistry datasets for reaction prediction.


high-throughput chemistry
direct to biology
high-throughput experimentation

Supplementary materials

Supporting Information
Details on the installation and running of PyParse, experimental procedures, analytical data for synthesized compounds and copies of NMR spectra for isolated compounds.

Supplementary weblinks


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