Automated LC-MS analysis and data extraction for high-throughput chemistry
Literature Information
Joseph Mason, David J. Fallon, Jacob T. Bush, Nicholas C. O. Tomkinson, Francesco Rianjongdee
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.
Recommended Journals

Topics in Catalysis

Biocatalysis and Biotransformation

Main Group Chemistry

Bioorganic & Medicinal Chemistry Letters

Critical Reviews in Solid State and Materials Sciences

Journal of the Indian Institute of Science

NDT & E International

Bioorganic & Medicinal Chemistry

Herald of the Russian Academy of Sciences

Polycyclic Aromatic Compounds
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