Generation of QSAR sets with a self-organizing map

Journal of Molecular Graphics & Modelling
Rajarshi GuhaPeter C Jurs

Abstract

A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon descriptors. The resultant classification is used to generate training, cross-validation (CV) and prediction sets for QSAR modeling using the ADAPT methodology. The results are compared to those of QSAR models generated using sets created by activity binning and a sphere exclusion method. The results indicate that the SOM is able to generate QSAR sets that are representative of the composition of the overall data set in terms of similarity. The resulting QSAR models are half the size of those published and have comparable RMS errors. Furthermore, the RMS errors of the QSAR sets are consistent, indicating good predictive capabilities as well as generalizability.

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Mar 27, 2007·Pharmaceutical Research·Gus R RosaniaKerby Shedden
Jul 28, 2012·International Journal of Molecular Sciences·Xue XuYang Ling
Jul 1, 2010·Expert Opinion on Drug Discovery·Chanin NantasenamatVirapong Prachayasittikul
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Jan 19, 2019·Journal of Chemical Information and Modeling·Beatrice PecoraroMatthew Traynor
Nov 29, 2005·Journal of Chemical Information and Modeling·Michael FernándezJulio Caballero

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