Global Optimization Approaches for Molecular and Protein Conformation Problems

基于全局优化算法和启发式的系统构象搜索是生物分子建模的有力工具。全局优化为寻找全局能量最小值提供了有效的机制，也为解决分子构象和分子相似与不相似引起的距离几何问题提供了新的方法。该项目将专注于设计、分析和实现高效算法，用于计算分子和蛋白质构象中一类多外部优化问题的全局最优解，特别是：给定蛋白质结构的多个自由度的非凸能量函数的全局优化。分子构象中距离几何问题的全局优化算法与启发式。求解分子相似和不相似问题的全局优化方法。计算生物学问题中的层次优化。处理这些优化问题的“全局方法”与其他传统的“局部方法”（例如基于梯度下降的算法）形成对比。后两种方法得到的解最多是局部最优的。然而，可以确定的是，上述问题具有不同于全局的指数级的局部解决方案。

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.