PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design

  • Oliver Korb
  • Thomas Stützle
  • Thomas E. Exner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


A central part of the rational drug development process is the prediction of the complex structure of a small ligand with a protein, the so-called protein-ligand docking problem, used in virtual screening of large databases and lead optimization. In the work presented here, we introduce a new docking algorithm called PLANTS (Protein-Ligand ANTSystem), which is based on ant colony optimization. An artificial ant colony is employed to find a minimum energy conformation of the ligand in the protein’s binding site. We present the effectiveness of PLANTS for several parameter settings as well as a direct comparison to a state-of-the-art program called GOLD, which is based on a genetic algorithm. Last but not least, results for a virtual screening on the protein target factor Xa are presented.


Root Mean Square Deviation Virtual Screening Rotatable Bond Heuristic Information Average Success Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oliver Korb
    • 1
  • Thomas Stützle
    • 2
  • Thomas E. Exner
    • 1
  1. 1.Theoretische Chemische DynamikUniversität KonstanzKonstanzGermany
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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