Abstract
High-throughput screening (HTS) is a core component for the identification of lead candidates in modern pharmaceutical drug discovery. It is a highly automated process that operates on an industrial scale. The current screening capacities of pharmaceutical HTS units are actually reaching tens of thousands compounds per day, and even this throughput performance is constantly challenged by advances in miniaturization and automation technology (Wildey et al. 1999; Burbaum 2000). The vast amount of information about structure-activity relationships (SAR) produced by HTS in pharmaceutical companies for lead identification has created a great need for fast, accurate, and fully automated data analysis tools. Manual processing of the resulting data or the application of conventional data analysis techniques is no viable option for logistical reasons. Hence, there is much current interest in novel analytical approaches that support the many facets of data analysis in HTS.
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Engels, M.F.M. (2003). Creating Knowledge from High-Throughput Screening Data. In: Waldmann, H., Koppitz, M. (eds) Small Molecule — Protein Interactions. Ernst Schering Research Foundation Workshop, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05314-0_7
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DOI: https://doi.org/10.1007/978-3-662-05314-0_7
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