Next, the 1 H NMR data was analyzed for conversion, where any residual aldehyde signals indicated that the dynamic covalent reaction had not gone to full conversion. Both cage peaks and any residual aldehyde were automatically identified, using the moc_nmr_analyser.py Python script. The script was based on that from cagey, but further adapted to include the NMR solvent acetonitrile-d3, as it was previously only suitable for chloroform-d. The user benefits from providing one path to the folder of the NMR raw data as the input, and then a CSV output labelled by the raw files title name is written containing the peak type, peak shift in ppm, and amplitude. If any residual aldehyde was observed, a further qualitative analysis was performed to determine the percentage of remaining aldehyde which was categorized and outputted as a JSON database file. If the relative percentage of the aldehyde to the largest cage or imine peak was below 1% it was categorized as 'minor', if between 1-5% it was categorized as 'still considered for scale-up', and if above 5% categorized as 'conversion not satisfactory'. This additional step is qualitative as no internal standard was used, but provides additional insight when identifying 'hits' to scale up. While this level of automated analysis is not suitable for full characterization and identification of the exact MOC topological outcome, it does streamline the screening process by giving an indication of whether a targeted species has formed and the conversion of the reaction (Figure ), saving researcher effort and time.