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

Understanding 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. While 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 dataset, we prioritized groups of compounds for further study, including a novel set of putative histone deacetylase inhibitors. We present here a general approach to analyzing SARs for large numbers of high‐ dimensional biological profiles using FPM and, in addition, ARM to automatically formulate SAR rules. Rules that connect chemical features to patterns in biological profiles are automatically identified and ranked by interestingness. We evaluated our method on gene‐ expression and cell‐morphology profiles for more than 30,000 compounds. The compound collection contains subsets representative of common screening libraries assembled from various sources as well as planned synthetic libraries of compounds with well‐defined structural relationships. We used different chemical and biological descriptors to tailor the general approach to specific requirements of the compound library.

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