A Semi-Definite programming-based Underestimation method for global optimization in molecular docking

The paper introduces a new global optimization method that is targeted to solve molecular docking problems, an important class of problems in computational biology. The search method is based on finding general convex quadratic underestimators to the binding energy function that is funnel-like. Finding the optimum underestimator requires solving a semi-definite programming problem, hence the name Semi-Definite programming based Underestimation (SDU). The optimal underestimator is used to bias sampling in the search region. A detailed comparison of SDU with a related method of Convex Global Underestimator (CGU), a discussion of the convergence properties of SDU, and computational results of the application of SDU to a number of rigid protein-protein docking problems are provided.