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Applications of Integrated Data Mining Methods to Exploring Natural Product Space for Acetylcholinesterase Inhibitors

[ Vol. 13 , Issue. 1 ]

Author(s):

Daniela Schuster, Lisa Kern, Dimitar P. Hristozov, Lothar Terfloth, Bruno Bienfait, Christian Laggner, Johannes Kirchmair, Ulrike Grienke, Gerhard Wolber, Thierry Langer, Hermann Stuppner, Johann Gasteiger and Judith M. Rollinger   Pages 54 - 66 ( 13 )

Abstract:


Nature, especially the plant kingdom, is a rich source for novel bioactive compounds that can be used as lead compounds for drug development. In order to exploit this resource, the two neural network-based virtual screening techniques novelty detection with self-organizing maps (SOMs) and counterpropagation neural network were evaluated as tools for efficient lead structure discovery. As application scenario, significant descriptors for acetylcholinesterase (AChE) inhibitors were determined and used for model building, theoretical model validation, and virtual screening. Topranked virtual hits from both approaches were docked into the AChE binding site to approve the initial hits. Finally, in vitro testing of selected compounds led to the identification of forsythoside A and (+)-sesamolin as novel AChE inhibitors.

Keywords:

Natural products, drug discovery, acetylcholinesterase, virtual screening, counterpropagation network, spinne, novelty detection

Affiliation:

Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria.



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