Macromolecular Modeling Optimization Algorithms for Macromolecular Modeling Global optimization techniques are central to the solution of macromolecular modeling and simulation problems, The aim of this project is to develop a high-performance environment for the Center for Computational Science and Technology at Argonne that supports large scale global optimization. We have done work on the determination of protein structures, ionic systems and molecular clusters.
Current work focuses on distance geometry optimization for protein structures.
An appealing idea for finding the global minimizer of a function is to transform the function into a smoother function with fewer local minimizers, apply an optimization algorithm to the transformed function, and trace the minimizers back to the original function. A transformed function is a coarse approximation to the original function, with small and narrow minimizers being removed while the overall structure of the function is maintained. This property allows a local minimization procedure to skip less interesting local minimizers and to concentrate on regions with low function values, where a global minimizer is most likely to be located. The smoothing transform, called the Gaussian transform, depends on a parameter that controls the degree of smoothing. The original function is obtained if the parameter is zero, while smoother functions are obtained as the parameter increases.