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Particle Swarm Optimization applied to Automated Docking

Automated docking of a ligand to a macromolecule

Particle Swarm Optimization Multi-objective PSO + Clustering Docking experiments Conclusion

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Automated Docking Predict binding of a ligand molecule to

a receptor macromolecule Minimize resulting binding energy

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Energy Evaluation

[Morris et al.]

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Autodock 3.05 Determine energies using trilinear

interpolation on precalculated grid maps

Minimize docking energy with various optimization techniques Simulated Annealing Genetic Algorithm with Local Search

Sum of energies is minimized

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Particle Swarm Optimization

Multi-dimensional, numerical optimization by a swarm of particles

Each particle has current position ,best position and velocity

Attracted by personal best positionand neighbourhood best position

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PSO Algorithm

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Clustering Particles are clustered into K separate

swarm

K-means Clustering m data-vectors are clustered into k

clusters Iteratively calculate centroids of each

cluster

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Multiple Objectives Optimize ,

simultaneously Find dominating solutions

Non-Dominated Front

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Clust-MPSO Update personal best position

Each swarm has non-dominated front is dominated if no particle is in Dominated swarms are relocated

Neighbourhood best particle Picked for several iterations

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1hvr Docking

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4cha Docking

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Convergence – 1hvr

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Convergence – 4cha

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Conclusions Application of PSO to Automated

Docking Optimization of two objectives Clustering to divide the search space