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Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

2001; American Chemical Society; Volume: 45; Issue: 1 Linguagem: Inglês

10.1021/jm010934d

1520-4804

Olivier Roche, Petra Schneider, Jochen Zuegge, Wolfgang Guba, Manfred Kansy, Alexander Alanine, Konrad Bleicher, Franck Danel, Eva-Maria Gutknecht, Mark Rogers‐Evans, Werner Neidhart, Henri Stalder, Michael Dillon, Eric Sjögren, Nader Fotouhi, Paul Gillespie, Robert A. Goodnow, William R. Harris, Phil Jones, Mikio Taniguchi, Shinji Tsujii, Wolfgang von der Saal, Gerd W. Zimmermann, Gisbert Schneider,

Metabolomics and Mass Spectrometry Studies

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.