Novel Methods for Prioritizing “Close-In” Analogs from Structure–Activity Relationship Matrices

Here
we describe the development of novel methods for compound
evaluation and prioritization based on the structure–activity
relationship matrix (SARM) framework. The SARM data structure allows
automatic and exhaustive extraction of SAR patterns from data sets
and their organization into a chemically intuitive scaffold/functional-group
format. While SARMs have been used in the retrospective analysis of
SAR discontinuity and identifying underexplored regions of chemistry
space, there have been only a few attempts to apply SARMs prospectively
in the prioritization of “close-in” analogs. In this
work, three new ways of prioritizing virtual compounds based on SARMs
are described: (1) matrix pattern-based prioritization, (2) similarity
weighted, matrix pattern-based prioritization, and (3) analysis of
variance based prioritization (ANV). All of these methods yielded
high predictive power for six benchmark data sets (prediction accuracy R2 range from 0.63 to 0.82), yielding confidence
in their application to new design ideas. In particular, the ANV method
outperformed the previously reported SARM based method for five out
of the six data sets tested. The impact of various SARM parameters
were investigated and the reasons why SARM-based compound prioritization
methods provide higher predictive power are discussed.