Cheminformatics is playing an ever-increasing role in small molecule drug discovery. The widespread use of high-throughput screening (HTS) and combinatorial chemistry techniques has led to the generation of large amounts of pharmacological data which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. This review focuses on recent advances in the field of substructure analysis, an increasingly popular data mining technique with applications at many levels of the discovery process, including HTS, compound library design, virtual screening and the prediction of biological activity.
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