Automated Structure-Activity Relationship Mining: Connecting Chemical Structure to Biological Profiles

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Automated Structure-Activity Relationship Mining: Connecting Chemical Structure to Biological Profiles

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Title: Automated Structure-Activity Relationship Mining: Connecting Chemical Structure to Biological Profiles
Author: Wawer, M. J.; Jaramillo, D. E.; Dan ik, V.; Fass, D. M.; Haggarty, Stephen John; Shamji, A. F.; Wagner, B. K.; Schreiber, Stuart L.; Clemons, P. A.

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Citation: Wawer, M. J., D. E. Jaramillo, Dan ik V., D. M. Fass, S. J. Haggarty, A. F. Shamji, B. K. Wagner, S. L. Schreiber, and P. A. Clemons. 2014. Automated Structure-Activity Relationship Mining: Connecting Chemical Structure to Biological Profiles. Journal of Biomolecular Screening 19, no. 5: 738–748. doi:10.1177/1087057114530783.
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Abstract: Understanding the structure–activity relationships (SARs) of small molecules is important for developing probes and novel therapeutic agents in chemical biology and drug discovery. Increasingly, multiplexed small-molecule profiling assays allow simultaneous measurement of many biological response parameters for the same compound (e.g., expression levels for many genes or binding constants against many proteins). Although such methods promise to capture SARs with high granularity, few computational methods are available to support SAR analyses of high-dimensional compound activity profiles. Many of these methods are not generally applicable or reduce the activity space to scalar summary statistics before establishing SARs. In this article, we present a versatile computational method that automatically extracts interpretable SAR rules from high-dimensional profiling data. The rules connect chemical structural features of compounds to patterns in their biological activity profiles. We applied our method to data from novel cell-based gene-expression and imaging assays collected on more than 30,000 small molecules. Based on the rules identified for this data set, we prioritized groups of compounds for further study, including a novel set of putative histone deacetylase inhibitors.
Published Version: doi:10.1177/1087057114530783
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34309453
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