Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Received October 16, 2003 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 IC 5  0  or K i  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.
['Andreas Teckentrup', 'Hans Briem', 'Johann Gasteiger']
I am writing a research paper on a idea Mining High‐Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits.
[{"from": "human", "value": "I am writing a research paper on a idea Mining High\u2010Throughput Screening Data of Combinatorial Libraries: Development of a Filter to Distinguish Hits from Nonhits."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Received October 16, 2003 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 IC 5  0  or K i  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."}]