EMBIO – Cambridge 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

EMBIO – Cambridge Automated Docking Predict binding of a ligand molecule to a receptor macromolecule Minimize resulting binding energy

EMBIO – Cambridge Energy Evaluation [Morris et al.]

EMBIO – Cambridge 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

EMBIO – Cambridge 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 position and neighbourhood best position

EMBIO – Cambridge PSO Algorithm

EMBIO – Cambridge 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

EMBIO – Cambridge Multiple Objectives Optimize, simultaneously Find dominating solutions Non-Dominated Front

EMBIO – Cambridge 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

EMBIO – Cambridge

1hvr Docking

EMBIO – Cambridge 4cha Docking

EMBIO – Cambridge Convergence – 1hvr

EMBIO – Cambridge Convergence – 4cha

EMBIO – Cambridge Conclusions Application of PSO to Automated Docking Optimization of two objectives Clustering to divide the search space