Applications of integrated data mining methods to exploring natural product space for acetylcholinesterase inhibitors.
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. Top-ranked 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.
Schuster
Daniela
D
Department of Pharmacognosy, Institute of Pharmacy, University of Innsbruck, Austria.
Kern
Lisa
L
Hristozov
Dimitar P
DP
Terfloth
Lothar
L
Bienfait
Bruno
B
Laggner
Christian
C
Kirchmair
Johannes
J
Grienke
Ulrike
U
Wolber
Gerhard
G
Langer
Thierry
T
Stuppner
Hermann
H
Gasteiger
Johann
J
Rollinger
Judith M
JM
D000110:Acetylcholinesterase / Q000737:chemistry / Q000378:metabolism*; D001688:Biological Products / Q000737:chemistry / Q000494:pharmacology*; D002800:Cholinesterase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D057225:Data Mining / Q000379:methods*; D055808:Drug Discovery; D008958:Models, Molecular
D001688:Biological Products; D002800:Cholinesterase Inhibitors; D000110:Acetylcholinesterase