Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists


DOI: 10.1007/s10822-009-9304-1

Cite this article as:
Krueger, B.A., Weil, T. & Schneider, G. J Comput Aided Mol Des (2009) 23: 869. doi:10.1007/s10822-009-9304-1


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 (GlycineB 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.


Drug discovery N-Methyl-d-Aspartate receptor GlycineB Bayesian classifier Pharmacophore Novelty detection Machine learning Molecular shape Docking Structure–activity relationship 

Supplementary material

10822_2009_9304_MOESM1_ESM.doc (249 kb)
Supplementary material 1 (DOC 249 kb)

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Institute of Organic Chemistry und Chemical BiologyJohann Wolfgang Goethe-UniversityFrankfurtGermany
  2. 2.Merz Pharmaceuticals GmbHChemical R&D–Drug DesignFrankfurtGermany
  3. 3.Department of ChemistryNational University of SingaporeSingaporeSingapore

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