To enable easier user interaction with the points in the scatterplot, the system offers a function for grouping neighboring points. Users can customize visual encodings of the points in the scatterplot. For example, the points can be sized by molecular weight or colored by group, as shown in the "Projection View" in Figure . Grouping and interactively changing the visual encoding of data points help users to explore patterns and find clusters in the data (T1). Using an encoding to visualize model performance metrics allows users to identify regions of the projection related to specific aspects of the model (T2). For example, if dark colors represent inaccurate prediction, users can quickly identify groups of dark compounds, analyze them and check whether there are patterns that correlate to the inaccurate predictions.