Abstract
Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The data sets have in common that they have been derived from compounds included in the ChEMBL database (release 17) for which high-confidence activity data are available. Thus, the activity data associated with MMP-based activity cliffs, SAR transfer series, and retrosynthetic MMPs cover the entire spectrum of current pharmaceutical targets. Our data sets are made freely available to the scientific community.
Highlights
The matched molecular pair (MMP) concept is widely applied in medicinal chemistry[1,2,3,4]
As a follow-up on the original publications in which MMP-cliffs[12], structure-activity relationship (SAR) transfer series[14], and retrosynthetic combinatorial analysis procedure (RECAP)-MMPs16 were introduced, all corresponding data sets have been re-generated on the basis of ChEMBL release 17, providing up-to-date versions for release
Leading to large potency differences that are captured by MMP
Summary
The matched molecular pair (MMP) concept is widely applied in medicinal chemistry[1,2,3,4]. An MMP is defined as a pair of compounds that are only distinguished by a structural modification at a single site[1], i.e., the exchange of a substructure, termed a chemical transformation[5]. MMPs are attractive tools for computational analysis because they can be algorithmically generated and they make it possible to associate defined structural modifications at the level of compound pairs with chemical property changes, including biological activity[2,3,4]. MMPs are usually chemically intuitive and accessible, which helps to bridge the gap between computational analysis and the practice of medicinal chemistry.
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