Development of a Virtual Screening Method for Identification of "Frequent Hitters" in Compound Libraries. Roche, O., Schneider, P., Zuegge, J., Guba, W., Kansy, M., Alanine, A., Bleicher, K., Danel, F., Gutknecht, E., Rogers-Evans, M., Neidhart, W., Stalder, H., Dillon, M., Sjogren, E., Fotouhi, N., Gillespie, P., Goodnow, R., Harris, W., Jones, P., Taniguchi, M., Tsujii, S., von der Saal, W., Zimmermann, G., & Schneider, G. J.~Med.~Chem., 45(1):137--142, 2002. doi abstract bibtex 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.
@article{Roche:2002fj,
Abstract = {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.},
Author = {Roche, Olivier and Schneider, Petra and Zuegge, Jochen and Guba, Wolfgang and Kansy, Manfred and Alanine, Alexander and Bleicher, Konrad and Danel, Franck and Gutknecht, Eva-Maria and Rogers-Evans, Mark and Neidhart, Werner and Stalder, Henri and Dillon, Michael and Sjogren, Eric and Fotouhi, Nader and Gillespie, Paul and Goodnow, Robert and Harris, William and Jones, Phil and Taniguchi, Mikio and Tsujii, Shinji and von der Saal, Wolfgang and Zimmermann, Gerd and Schneider, Gisbert},
Date-Added = {2007-12-11 17:01:03 -0500},
Date-Modified = {2008-04-11 21:05:29 -0400},
Doi = {10.1021/jm010934d},
Journal = {J.~Med.~Chem.},
Keywords = {frequent hitter; promiscuous ;HTS},
Number = {1},
Pages = {137--142},
Title = {Development of a Virtual Screening Method for Identification of "Frequent Hitters" in Compound Libraries.},
Volume = {45},
Year = {2002},
Bdsk-Url-1 = {http://dx.doi.org/10.1021/jm010934d}}
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