Evolutionary Hybrids for Flexible Ligand Docking

Molecular docking methods are valuable tools for structure-based drug discovery that can be effectively used to identify promising drug candidates. Solving docking problems is particularly challenging when conformational flexibility is included in the docking model. I will describe one such model, AutoDock, and I will review the hybrid evolutionary algorithms that have been used to successfully solve the challenging docking problems in AutoDock.

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Table of Contents

Evolutionary Hybrids for Flexible Ligand Docking

Overview

Evolutionary Algorithms Overview

Evolutionary Algorithms Search

Evolutionary Algorithms Main Loop

Local Search

EA Hybrids with Local Search Motivation

EA Hybrids Model

EA Hybrids Model (contíd)

EA Hybrids Design

EA Hybrids Design (contíd)

Hybrid Dynamics Fitness Transformation

Hybrid Dynamics Refinement Operator

Hybrid Dynamics Repair Operator

Applications of EA Hybrids

Flexible Drug Docking Motivation

The AutoDock Potential Energy Model

Computing Potential Energies

Docking Formulation

Docking Formulation (contíd)

Optimization Methods

Evolutionary Programming

Evolutionary Pattern Search

EA Hybrids for Docking

Solis-Wets Local Search

Solis-Wets Local Search (contíd)

Pattern Search Local Search

Pattern Search Local Search (contíd)

Experiments

Relative Performance

Relative Performance (contíd)

Length of Optimization

Future Work

Acknowledgements

Author: William E. Hart
Sandia National Labs.

William Hart received a Ph.D. in Computer Science from U.C. San Diego in 1994. Since graduating, he has worked in the Computation, Computers, and Math Center at Sandia National Laboratories, Albuquerque. William has conducted research in diverse areas such as protein structure prediction, parallel optimization and evolutionary algorithms. His work with evolutionary algorithms (EAs) has focused on convergence analyses of EAs on continuous domains, and on hybrid EAs that use local search.