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DrugscoreMapsvisualizing similarities in protein-ligand interactions

A new approach will be presented that graphical evaluate Drugscore Fingerprints [1] using emergent self-organizing maps (ESOMs) [2] for clustering of binding geometries to identify similarities among protein-ligand interactions in data sets of protein-ligand poses. The result of the clustering shows a landscape of valleys and mountains and is easy to interpret. Similar binding geometries are clustered together within a valley surrounded by mountains. Colouring of the data points based on DrugscoreCSD ranks [3] or known affinity data reveals additional information.

A survey of the Wang [4] and the Astex Diverse Dataset [5] exhibits that DrugscoreMaps is useful for the evaluation of docking poses and it supports the search for the correct low energy binding mode. DrugscoreMaps combines the information about similar protein-ligand poses with the information about interaction patterns (represented by Drugscore). Clearly separated clusters with high-ranked docking poses are an indication of good binding geometries and, in contrast, a lack of clustering seems to indicate a failing of the docking procedure. Additionally, bad geometries with a high rank and situations were the scoring function fails can be identified. Furthermore, an analysis of a successfully used QSAR dataset reveals a first indication that DrugscoreMaps is also useful for visualization of structure-activity landscape within this dataset. Compared to other fingerprint based methods, DrugscoreMaps (using DrugscoreFP) integrates protein information for creating these structure-activity landscapes.

DrugscoreMaps benefits by ease of visualization. Protein-ligand similarity is included in one image that gives you a direct overview of the used dataset. One gains information about similar high-ranked docking poses and dissimilar docking poses or an overview over the structure-activity landscape without looking at all docking solutions or protein-ligand poses.


  1. Pfeffer P, Neudert G, Klebe G: Chemistry Central Journal. 2008, 2 (1): 16-10.1186/1752-153X-2-S1-S16.

    Article  Google Scholar 

  2. Ultsch A: Proceedings of Workshop on Self-Organizing Maps (WSOM); Kyushu, Japan. 2003, 225-230.

    Google Scholar 

  3. Velec HFG, Gohlke H, Klebe G: J Med Chem. 2005, 48 (20): 6296-6303. 10.1021/jm050436v.

    Article  CAS  Google Scholar 

  4. Hartshorn MJ, Verdonk ML, et al: J Med Chem. 2007, 50 (4): 726-741. 10.1021/jm061277y.

    Article  CAS  Google Scholar 

  5. Hillebrecht A, Supuran CT, Klebe G: Chem Med Chem. 2006, 1 (8): 839-853.

    Article  CAS  Google Scholar 

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Koch, O., Neudert, G. & Klebe, G. DrugscoreMapsvisualizing similarities in protein-ligand interactions. Chemistry Central Journal 3 (Suppl 1), P61 (2009).

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