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Old 06-29-2017, 06:02 AM
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rss Novel Methods for Prioritizing "Close-In" Analogs from SAR Matrices.

Novel Methods for Prioritizing "Close-In" Analogs from SAR Matrices.

J Chem Inf Model. 2017 Jun 28;:

Authors: Zhang L, Johnson K, Starr JT, Milbank JBJ, Kuhn AM, Poss CS, Stanton RV, Shanmugasundaram V

Abstract
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 0.63-0.82), 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 datasets tested. The impact of various SARM parameters investigated and the reasons why SARM-based compound prioritization methods provide higher predictive power are discussed.


PMID: 28657313 [PubMed - as supplied by publisher]



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