Matcher makes the MMP data of large datasets transparent and interrogable with fast iterable queries. Input and output are fully integrated within a single browser view. While the focus of this design discussion is on the experience for nonexpert users, expert users can programmatically leverage the functionality described below, because all MMP logic is handled through (API) endpoints from a backend server that connects to a database. The basic flow of the application is illustrated in Figure , with a query on MMPs derived from approximately 20,000 compounds from ChEMBL with data for inhibition of hERG and/or CYP3A4. To motivate the query shown in Figure , a hypothetical scenario that is representative of typical drug structure optimization is as follows: A drug designer wants to increase IC50 for a molecule's inhibition of CYP3A4 by changing the molecule's methoxy (OCH 3 ) to a different group that contains (and is attached by) a nitrogen atom to the core structure in red. The designer wants to understand all precedent for such transforms, filtering only for such transforms that occur on the specific red-highlighted substructure of interest, which the designer considers to be essential context. Based on the query results, the designer may consider executing the transform highlighted in pink in Figure , panel b, by making the compound that contains the specified nitrogen-containing group instead of methoxy, because of the observed trend for CYP3A4 IC50 to increase with this change. With Matcher, the designer can trivially "drill down" into the relevant data for this transform, to visualize the distribution of changes in CYP3A4 IC50 across all relevant MMPs (Figure , panel c, left), as well as structural features and diversity within the MMPs themselves (Figure , panel c, right). The designer would likely weigh this information together with a multitude of other factors, such as output from additional computational models, difficulty of chemical synthesis, etc.