Systematic conformational search based on global optimization algorithms and heuristics is a powerful tool in modeling of biomolecules. Global optimization provides efficient mechanisms for finding global energy minima as well as new approaches for solving distance geometry problems arising in molecular conformation and molecular similarity and dissimilarity. This project will focus on the design, analysis, and implementation of efficient algorithms for computing the global optima for a class of multi-external optimization problems in molecular and protein conformation, specifically:
Global optimization of nonconvex energy functions given the many degrees of freedom available to protein structures.
Global optimization algorithms and heuristics for the distance geometry problem in molecular conformation.
Global optimization approaches for solving molecular similarity and dissimilarity problems.
Hierarchical optimization in computational biology problems.
The "global approach" to treating these optimization problems is in contrast to other traditional "local approaches", for example algorithms based on gradient descent. The solutions obtained by the latter approaches are at most locally optimal. However it is well established that the above problems have an exponential number of local solutions different from the global.
