Matched Molecular Series: Measuring SAR Similarity

Suggesting novel
compounds to be made on the basis of similarity
to a previously seen structure–activity relationship (SAR)
requires a measure for SAR similarity. While SAR similarity has intuitively
been used by medicinal chemists for decades, no systematic comparison
of candidate similarity metrics has been published to date. With this
publication, we attempt to close that gap by providing a statistical
framework that allows comparison of SAR similarity metrics by their
ability to rank series that provide the best activity prediction of
novel substituents. This prediction is a result of a two-step process
that involves (a) judging the similarity between series and (b) transferring
the SAR from one series to the other. We tested several SAR similarity
metrics and found that a centered RMSD (cRMSD) in combination with
a lineaar-regression-based prediction interpolation ranks SAR profiles
best. Based on that ranking we can, with a given confidence, suggest
novel substituents to be tested. The superiority of the cRMSD can
be explained on the basis of experimental uncertainty of affinity
data and measured affinity differences. The ability to measure SAR
similarity is central to applications like matched molecular series
(MMS) analysis, whose applicability depends on whether there is a
potential for SAR transferability between series. With the new SAR
similarity metric introduced here, we show how MMS can be used in
a medicinal chemistry setting as an idea generator and a semiquantitative
prediction tool.