Poster: Polarization Energy on a Cluster of Multicores

When a molecule experiences an electric field, its charge distribution is relaxed in response to that field. The energy associated with this relaxation is known as the polarization energy . Computing the polarization energy between a ligand (i.e., a small molecule such as a drug molecule) and a receptor (e.g., a virus molecule) is of utmost importance in drug design, protein-protein docking, virus/bacterium cell analysis, molecular dynamics simulations for determining the molecular conformation with minimal total free energy. We have implemented distributed-memory and distributed shared-memory parallel algorithms for approximating polarization energy of a molecule by extending a prior work for shared-memory (multicore) architectures. This is an octree-based hierarchical algorithm, built on GreengardRokhlin type near and far decomposition of data points (i.e., atoms and points sampled from the molecular surface) which calculates the polarization energy of a molecule using the r⁶ approximation of Generalized Born (GB) Radii of atoms. Both Poisson-Boltzmann (PB) GeneralizedBorn (GB) models can be used for approximating polarization energy. However, due to high computational costs PB method is rarely used for large molecules such as proteins.