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  • Poster presentation
  • Open Access

Cavka – a new automatic pharmacophore elucidation method in progress

Journal of Cheminformatics20113 (Suppl 1) :P31

  • Published:


  • Standard Tool
  • Pharmacophore Model
  • User Intervention
  • Biological Testing
  • Electronic Feature

Three dimensional pharmacophore models can be considered as an ensemble of steric and electronic features in space, which are necessary to ensure intermolecular interaction with a specific target in order to trigger or to block biological activity [1]. By identifying these features, a 3D pharmacophore model can be built in order to screen multi-conformatorial databases with the aim to detect compounds matching the pharmacophoric hypothesis and subsequently submit them to a biological testing. Even if a 3D crystal structure is at hand, the creation of a reliable pharmacophore model remains a challenging task.

CavKA (Cavity Knowledge Acceleration), our own in-house strategy employs the information of Co-crystallised ligand-receptor complexes for an automatic pharmacophore creation. Ligand features interacting with the binding site are detected and Grid [2] force field information is additionally taken into account as to weight and prioritize the identified features in question, to transform them into a pharmacophore model without any user intervention.

Our method is compared to LigandScout [3] and a custom MOE [4] implementation, similar to LigandScout, two powerful standard tools. Both are identifying ligand-receptor interactions to highlight important ligand features to be selected for creating pharmacophore models automatically. The performance is evaluated in a retrospective screening on the FieldScreen [5] dataset outlining strengths, weaknesses and as well as similarities of each method for the scrutinized targets.

Authors’ Affiliations

Pharmaceutical Chemistry, University of Technology, 38106 Braunschweig, Germany


  1. IUPAC :Google Scholar
  2. Grid 22a, molecular discovery : []
  3. Ligandscout 3.0, inte:ligand : []
  4. Moe 2009.10, chemcomp : []
  5. Cheeseright TJ, Mackey MD, Melville JM, Vinter JG: FieldScreen: virtual screening using molecular fields. Application to the DUD data set. J Chem Inf Model. 2008, 48: 2108-2117. 10.1021/ci800110p.View ArticleGoogle Scholar


© Koelling and Baumann; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.