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- W1964513093 abstract "Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html." @default.
- W1964513093 created "2016-06-24" @default.
- W1964513093 creator A5027269400 @default.
- W1964513093 creator A5073011301 @default.
- W1964513093 date "2009-01-22" @default.
- W1964513093 modified "2023-10-06" @default.
- W1964513093 title "Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data" @default.
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- W1964513093 doi "https://doi.org/10.1021/ci8002649" @default.
- W1964513093 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19434821" @default.
- W1964513093 hasPublicationYear "2009" @default.
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