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Structure-Based Pharmacophores

Why Use Structure-Based Pharmacophores?

Pharmacophores represent chemical functions, valid not only for the currently bound, but also unknown molecules. Due to its simplicity, this method is computationally very efficient and is subsequently exceptionally well suited for virtual screening of large compound libraries. Pharmacophore modeling is universal, comprehensive and editable: Selectivity-tuning by adding or omitting feature constraints provides a plenitude of manipulation options.
The following figures show the active site of CDK2 with ligand LS2 (PDB 1KE6) and the respective pharmacophore model generated by LigandScout. The program automatically searches for interactions between ligands and the macromolecule.
Ligand LS2 of PDB entry 1KE6 in the active site
Figure 6.1. Ligand LS2 of PDB entry 1KE6 in the active site

Structure-based pharmacophore model generated by LigandScout
Figure 6.2. Structure-based pharmacophore model generated by LigandScout

In this example, LigandScout suggests an eight feature pharmacophore model consisting of three hydrophobic features (yellow spheres), two hydrogen bond donor features (green vectors) and three hydrogen bond acceptors (red vectors). This pharmacophore model represents a highly potent filter for in-silico screening of new CDK2 ligands.

Basic Workflow

The basic workflow for structure-based pharmacophore model generation based on PDB complexes consists of three main steps:
  1. Extraction, identification, and interpretation of the ligand (hybridization states, bonds)
  2. Deduction of the pharmacophore
  3. Visualization, user interaction, and export for virtual screening

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Page designed & authored by G. Wolber

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