A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. Roche, O., Trube, G., Zuegge, J., Pflimlin, P., Alanine, A., & Schneider, G. ChemBioChem, 3(5):455--459, May, 2002. doi abstract bibtex A computer-based method has been developed for prediction of the hERG (human ether-à-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design.
@article{Roche:2002uq,
Abstract = {A computer-based method has been developed for prediction of the hERG (human ether-{\`a}-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 \% of the nonblocking agents and 71 \% of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design.},
Author = {Roche, Olivier and Trube, Gerhard and Zuegge, Jochen and Pflimlin, Pascal and Alanine, Alexander and Schneider, Gisbert},
Date-Added = {2011-11-22 09:34:39 -0500},
Date-Modified = {2011-11-22 09:35:09 -0500},
Doi = {10.1002/1439-7633(20020503)3:5\<455::AID-CBIC455\>3.0.CO;2-L},
Journal = {{ChemBioChem}},
Journal-Full = {Chembiochem : a European journal of chemical biology},
Keywords = {herg;virtual screen;},
Mesh = {Cation Transport Proteins; Combinatorial Chemistry Techniques; DNA-Binding Proteins; Databases, Factual; Drug Design; Ether-A-Go-Go Potassium Channels; Humans; Linear Models; Molecular Structure; Neural Networks (Computer); Nonlinear Dynamics; Potassium Channels; Potassium Channels, Voltage-Gated; Structure-Activity Relationship; Trans-Activators},
Month = {May},
Number = {5},
Pages = {455--459},
Pmid = {12007180},
Pst = {ppublish},
Title = {A virtual screening method for prediction of the {hERG} potassium channel liability of compound libraries},
Volume = {3},
Year = {2002},
Bdsk-Url-1 = {http://dx.doi.org/10.1002/1439-7633(20020503)3:5%5C<455::AID-CBIC455%5C>3.0.CO;2-L}}
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Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. 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Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 \\% of the nonblocking agents and 71 \\% of the hERG channel blockers were correctly classified. 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