Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

doi:10.3390/91201148

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Molecules 2004, 9(12), 1148-1159; doi:10.3390/91201148

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Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

Jaroslaw Polanski * , Andrzej Bak, Rafal Gieleciak and Tomasz Magdziarz

Department of Organic Chemistry, Institute of Chemistry, University of Silesia, PL-40-006 Katowice Poland

* Author to whom correspondence should be addressed.

Received: 31 May 2004 / Accepted: 21 October 2004 / Published: 31 December 2004

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Abstract: We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.

Keywords: Self-organizing neural network; 3D QSAR; 4D QSAR; SOM-4D QSAR; CoMSA.

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MDPI and ACS Style

Polanski, J.; Bak, A.; Gieleciak, R.; Magdziarz, T. Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors. Molecules 2004, 9, 1148-1159.

AMA Style

Polanski J., Bak A., Gieleciak R., Magdziarz T. Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors. Molecules. 2004; 9(12):1148-1159.

Chicago/Turabian Style

Polanski, Jaroslaw; Bak, Andrzej; Gieleciak, Rafal; Magdziarz, Tomasz. 2004. "Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors." Molecules 9, no. 12: 1148-1159.

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