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Title: Comparative virtual screening and novelty detection for NMDA-Glycine B antagonists
Authors: Krueger, B.A.
Weil, T. 
Schneider, G.
Keywords: Bayesian classifier
Drug discovery
Machine learning
Molecular shape
N-Methyl-d-Aspartate receptor
Novelty detection
Structure-activity relationship
Issue Date: Dec-2009
Citation: Krueger, B.A., Weil, T., Schneider, G. (2009-12). Comparative virtual screening and novelty detection for NMDA-Glycine B antagonists. Journal of Computer-Aided Molecular Design 23 (12) : 869-881. ScholarBank@NUS Repository.
Abstract: Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine B site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes. © 2009 Springer Science+Business Media B.V.
Source Title: Journal of Computer-Aided Molecular Design
ISSN: 0920654X
DOI: 10.1007/s10822-009-9304-1
Appears in Collections:Staff Publications

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