To solve recurring problems in drug discovery, matched molecular pair (MMP) analysis is used to understand relationships between chemical structure and function. For the MMP analysis of large datasets (>10,000 compounds), available tools lack flexible search and visualization functionality and require computational expertise. Here we present Matcher, an open-source application for MMP analysis, with novel search algorithms and fully automated querying-to-visualization that requires no programming expertise. Matcher enables unprecedented control over the search and clustering of MMP transformations based on both variable fragment and constant environment structure, which is critical for disentangling relevant and irrelevant data to a given problem. Users can exert such control through a built-in chemical sketcher, and with a few mouse clicks can navigate between resulting MMP transformations, statistics, property distribution graphs and structures with raw experimental data, for confident and accelerated decision making. Matcher can be used with any collection of structure/property data; here we demonstrate usage with a public ChEMBL dataset of about 20,000 small molecules with CYP3A4 and/or hERG inhibition data. Users can reproduce all examples demonstrated herein via unique links within Matcher’s interface – a functionality that anyone can use to preserve and share their own analyses. Matcher and all its dependencies are open-source with permissive licenses and trivial containerized deployment, and is freely available at https://github.com/Merck/Matcher. Matcher makes large structure/property datasets more transparent than ever before and accelerates the data-driven solution of common problems in drug discovery.
Matcher: An Open-Source Application for
Translating Large Structure/Property Datasets into
Insights for Drug Design
27 October 2022, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.