title: Mining high-throughput screening data of combinatorial libraries: development of a filter to distinguish hits from nonhits.
abstract: Kohonen neural networks generate projections of large data sets defined in high-dimensional space. The resulting self-organizing maps can be used in many applications in the drug discovery process, such as to analyze combinatorial libraries for their similarity or diversity and to select descriptors for structure-activity relationships. The ability to investigate thousands of compounds in parallel also allows one to conduct a study based on single-dose experiments of high-throughput screening campaigns, which are known to have a greater uncertainty than IC50 or Ki values. This is demonstrated here for a data set of 5513 compounds from one combinatorial library. Furthermore, a method was developed that uses self-organizing maps not only as an indicator of structure-activity relationships, but as the basis of a classification system allowing predictive modeling of combinatorial libraries.
authors.affiliation: Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, D-88397 Biberach, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Teckentrup
authors.forename: Hans
authors.initials: H
authors.lastname: Briem
authors.forename: Johann
authors.initials: J
authors.lastname: Gasteiger