As the use of high-throughput screening systems becomes more routine in the drug discovery process, there is an increasing need for fast and reliable analysis of the massive amounts of the resulting data. At the forefront of the methods used is data reduction, often assisted by cluster analysis. Activity thresholds reduce the data set under investigation to manageable sizes while clustering enables the detection of natural groups in that reduced subset, thereby revealing families of compounds that exhibit increased activity toward a specific biological target. The above process, designed to handle primarily data sets of sizes much smaller than the ones currently produced by high-throughput screening systems, has become one of the main bottlenecks of the modern drug discovery process. In addition to being fragmented and heavily dependent on human experts, it also ignores all screening information related to compounds with activity less than the threshold chosen and thus, in the best case, can only hope to discover a subset of the knowledge available in the screening data sets. To address the deficiencies of the current screening data analysis process the authors have developed a new method that analyzes thoroughly large screening data sets. In this report we describe in detail this new approach and present its main differences with the methods currently in use. Further, we analyze a well-known, publicly available data set using the proposed method. Our experimental results show that the proposed method can improve significantly both the ease of extraction and amount of knowledge discovered from screening data sets.

A public bacterial mutagenicity database was classified into 2-D structural families using a set of specific algorithms and clustering techniques that find overlapping classes of compounds based upon chemical substructures. Structure-activity relationships were learned from the biological activity of the compounds within each class and used to identify rules that define substructures potentially responsible for mutagenic activity. In addition, this method of analysis was used to compare the pharmacologically relevant substructure of test compounds with their potential toxic substructures making this a potentially valuable in silico profiling tool for lead selection and optimization.

The routine use of high-throughput screening (HTS) systems in the drug discovery process has resulted in an increasing need for fast, reliable analysis of massive amounts of data. A new automated multidomain clustering method that thoroughly analyzes screening data sets is used to examine both the active and the inactive compounds in a well-known, publicly available data set based on primary screening. Large and small compound sets that defined both chemical families and potential pharmacophore points were discovered. The detection of structure-activity relationships (SAR), aided by the unique classification method, is described in this article.

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