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Exploration of Cluster Structure-Activity Relationship Analysis in Efficient High-Throughput Screening

Sequential screening has become increasingly popular in drug discovery. It iteratively builds quantitative structure-activity relationship (QSAR) models from successive high-throughput screens, making screening more effective and efficient. We compare cluster structure-activity relationship analysis (CSARA) as a QSAR method with recursive partitioning (RP), by designing three strategies for sequential collection and analysis of screening data. Various descriptor sets are used in the QSAR models to characterize chemical structure, including high-dimensional sets and some that by design have many variables not related to activity. The results show that CSARA outperforms RP. We also extend the CSARA method to deal with a continuous assay measurement.