Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1894-1899 |
| Number of pages | 6 |
| Journal | Digital Discovery |
| Volume | 2 |
| Issue number | 6 |
| Early online date | 19 Oct 2023 |
| DOIs | |
| Publication status | E-pub ahead of print - 19 Oct 2023 |
Funding
H. W. and R. P. T are grateful to GSK for a studentship via the GSK/University of Strathclyde Centre for Doctoral Training in Synthetic and Medicinal Chemistry. The authors thank the EPSRC for funding via Prosperity Partnership EP/S035990/1 and GSK for financial support. The authors are grateful to Andy Hobbs, Stephen Besley, Adrian Dunn, Sean Lynn, Michael Brown, and Omar Rahman in the Discovery Analytical department at GSK for the maintenance of open-access LCMS machines, the purification of regioisomeric products 4.1 and 4.2 and the acquisition of NMR and mass spectrometry data.
Keywords
- high-throughput
- plate data
- reaction prediction
- software package