Data Mining in Drug Discovery and Development

Ping Zhang, IBM T.J. Watson Research Center, USA
Lun Yang, GlaxoSmithKline, USA

Abstract:
Increasingly, effective drug discovery and development involve the searching and data mining of large amounts of heterogeneous information from many sources covering the domains of chemistry, biology and pharmacology amongst others. In this tutorial, we provide a review of the publicly-available large-scale databases relevant to drug discovery, describe the kinds of data mining approaches that can be applied to them, and identify directions for future research. Many of those insights come from drug discovery community, which is highly related to data mining but focuses on bioinformatics and/or cheminformatics specifics. We survey various related articles from data mining venues as well as from bioinformatics/cheminformatics venues to share with the audience key problems and trends in drug discovery research, with different applications such as drug combinations, drug repositioning, personalized medicine, and payer evidence.

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