ACM Computing Surveys (CSUR)
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Evolutionary Algorithms in Drug Design
Natural Computing: an international journal
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
PLANTS: application of ant colony optimization to structure-based drug design
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Optimization in water systems: a PSO approach
Proceedings of the 2008 Spring simulation multiconference
Multi-objective particle swarm optimization algorithm based on game strategies
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
Two-stage fuzzy stochastic programming with Value-at-Risk criteria
Applied Soft Computing
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Bonding as a swarm: applying bee nest-site selection behaviour to protein docking
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
Molecular docking with opposition-based differential evolution
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Identification of surgical practice patterns using evolutionary cluster analysis
Mathematical and Computer Modelling: An International Journal
Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
The molecular docking problem is to find a good position and orientation for docking a small molecule (ligand) to a larger receptor molecule. In the first part of this paper we propose a new algorithm for solving the docking problem. This algorithm - called ClustMPSO - is based on Particle Swarm Optimization (PSO) and follows a multi-objective approach for comparing the quality of solutions. For the energy evaluation the algorithm uses the binding free energy function that is provided by the Autodock 3.05 tool. The experimental results show that ClustMPSO computes a more diverse set of possible docking conformations than the standard Simulated Annealing and Lamarckian Genetic Algorithm that are incorporated into Autodock. Moreover, ClustMPSO is significantly faster and more reliable in finding good solutions. In the second part of this paper a new approach for the prediction of a docking trajectory is proposed. In this approach the ligand is ''un-docked'' via a controlled random walk that can be biased into a given direction and where only positions are accepted that have an energy level that is below a given threshold.