Quantitative
Structure–Activity Relationship
Models of Chemical Transformations from Matched Pairs Analyses
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Abstract
The
concepts of activity cliffs and matched molecular pairs (MMP)
are recent paradigms for analysis of data sets to identify structural
changes that may be used to modify the potency of lead molecules in
drug discovery projects. Analysis of MMPs was recently demonstrated
as a feasible technique for quantitative structure–activity
relationship (QSAR) modeling of prospective compounds. Although within
a small data set, the lack of matched pairs, and the lack of knowledge
about specific chemical transformations limit prospective applications.
Here we present an alternative technique that determines pairwise
descriptors for each matched pair and then uses a QSAR model to estimate
the activity change associated with a chemical transformation. The
descriptors effectively group similar transformations and incorporate
information about the transformation and its local environment. Use
of a transformation QSAR model allows one to estimate the activity
change for novel transformations and therefore returns predictions
for a larger fraction of test set compounds. Application of the proposed
methodology to four public data sets results in increased model performance
over a benchmark random forest and direct application of chemical
transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA)