Particle Swarm Optimization applied to Automated Docking

1. 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

2. Automated Docking • Predict binding of a ligand molecule to a receptor macromolecule • Minimize resulting binding energy

3. Energy Evaluation [Morris et al.]

4. 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

5. 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

6. PSO Algorithm

7. 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

8. Multiple Objectives • Optimize , simultaneously • Find dominating solutions • Non-Dominated Front

9. 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

11. 1hvr Docking

12. 4cha Docking

13. Convergence – 1hvr

14. Convergence – 4cha

15. Conclusions • Application of PSO to Automated Docking • Optimization of two objectives • Clustering to divide the search space