Validation of the general purpose Tripos 5.2 force field
Journal of Computational Chemistry
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Future Generation Computer Systems
Ant Colony Optimization
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Advances in Engineering Software
An analysis of communication policies for homogeneous multi-colony ACO algorithms
Information Sciences: an International Journal
Bonding as a swarm: applying bee nest-site selection behaviour to protein docking
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Molecular docking with opposition-based differential evolution
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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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.