We take a sample and re-heat the plate. For the nal sample, we quench the reaction mixtures with aqueous acetonitrile and draw an aliquot. LCMS sample submission relies on sequence les which are also generated while saving the plate. These contain molecular formulas of all the reaction's starting materials and expected products. When the LCMS-sequence is nished, the resulting text le is automatically uploaded to the Google Cloud Platform, imported into the database and a noti cation sent using Google Chat. Other analytical data, for example originating from SFC, GC or HPLC, can be imported using the Google Sheet by copying and pasting a peak list. Once the import is nished, users can analyze the data in Spot re. All peaks in which the LCMS detected one of the speci ed compounds are automatically tagged with the corresponding structure (Figure ). Since not all assignments are correct and not all compounds will ionize all the time, the users can mark peaks and tag them manually based on retention time or the masses present. In the visualization Mass over Time (Figure , bottom right), the ve most intense mass signals of all peaks are displayed as a function of retention time. Therefore, the combined dataset can be analyzed by retention time, mass or both. The automatic drill down makes it easy to recognize isotope patterns, identify all peaks that contain certain masses and thus assign structures. 17 Based on the peak assignments the Spot re tool automatically generates a pie chart visualization that displays the composition of all vials on the plate. If an internal standard is present, the tool will also generate relative yields for each component in each vial from the actual weights dosed of limiting starting material and internal standard as well as the respective peak areas (Figure ). These yields will be absolute, if a reference sample of the component in question together with the internal standard was measured. It also generates simple kinetics by displaying the change in yield over time. This is useful to track reaction progress and detect decomposition. The Spot re tool also visualizes the relationship between calculated yields and peak areas. Outliers will show up as data points that don't obey the commonly observed linear relationship. These often result from issues in peak integration or sampling errors. If we detect outliers, we tag them and the system attempts to correct them. It will also display the actual weights We recorded a live demonstration of the platform which can be found here: dosed as well as the equivalents relative to limiting starting material for each vial. This feature enables the explanation of anomalies in reaction performance caused by dosing errors. Knowing what are the most bene cial reaction inputs can be dif cult when several plates are run for a chemical transformation. For that purpose, our Spot re tool offers a set of visualizations in which the user can evaluate all plates at once (Figure ). The user has the option to select one or more desired and undesired compounds, along with one or more types of reaction inputs such as catalysts, solvents, or bases. Spot re will then show how much any of these components in uence the formation of the desired and undesired components. In a separate visualization, the tool will calculate a simple ratio or enantiomeric/diastereomeric excess of desired to undesired compounds. When going beyond a single transformation, the tool offers full substructure search and drilldown into all previous screening campaigns (Figure ). This is helpful for plate design, but also for deriving trends and consulting with customers on next steps. This tool provides backlinks to detailed results as well as a peek into the best conditions identi ed for each transformation and a rating on how successful the screening was deemed to be. A similar tool exists that provides sub-structure search and drilldown for the compound inventory including available batches, amounts and solutions.