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Mining High-Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits
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Section: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.
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This article has been cited by 6 ACS Journal articles (5 most recent appear below).
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Of Molecules and Humans
Johann GasteigerJournal of Medicinal Chemistry2006 49 (22), 6429-6434Of Molecules and Humans
Johann GasteigerJournal of Medicinal Chemistry2006 49 (22), 6429-6434
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- Published In Issue March 22, 2004
- Received October 16, 2003
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