Bio-inspired algorithms applied to molecular docking simulations

Curr Med Chem. 2011;18(9):1339-52. doi: 10.2174/092986711795029573.


Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • 3-Phosphoshikimate 1-Carboxyvinyltransferase / chemistry
  • 3-Phosphoshikimate 1-Carboxyvinyltransferase / metabolism
  • Algorithms*
  • Bacterial Proteins / chemistry*
  • Combinatorial Chemistry Techniques
  • Computer Simulation
  • Mycobacterium tuberculosis / enzymology*
  • Neural Networks, Computer
  • Protein Structure, Tertiary
  • Purine-Nucleoside Phosphorylase / chemistry
  • Purine-Nucleoside Phosphorylase / metabolism


  • Bacterial Proteins
  • Purine-Nucleoside Phosphorylase
  • 3-Phosphoshikimate 1-Carboxyvinyltransferase