Automated LC-MS analysis and data extraction for high-throughput chemistry

Joseph Mason, Harry Wilders, David J. Fallon, Ross P. Thomas, Jacob T. Bush, Nicholas C. O. Tomkinson, Francesco Rianjongdee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
9 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1894-1899
Number of pages6
JournalDigital Discovery
Issue number6
Early online date19 Oct 2023
Publication statusE-pub ahead of print - 19 Oct 2023


  • high-throughput
  • plate data
  • reaction prediction
  • software package


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