| Abstract
| - Sequential screening has become increasingly popular in drug discovery. It iteratively builds quantitativestructure−activity relationship (QSAR) models from successive high-throughput screens, making screeningmore effective and efficient. We compare cluster structure−activity relationship analysis (CSARA) as aQSAR method with recursive partitioning (RP), by designing three strategies for sequential collection andanalysis of screening data. Various descriptor sets are used in the QSAR models to characterize chemicalstructure, 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 continuousassay measurement.
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