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- W2071166181 abstract "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." @default.
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- W2071166181 date "2002-05-03" @default.
- W2071166181 modified "2023-10-16" @default.
- W2071166181 title "A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries" @default.
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- W2071166181 doi "https://doi.org/10.1002/1439-7633(20020503)3:5<455::aid-cbic455>3.0.co;2-l" @default.
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