A parallel version of the Branch & Prune algorithm for the Molecular Distance Geometry Problem

We consider the Molecular Distance Geometry Problem (MDGP), which is the problem of finding the conformation of a molecule from some known distances between its atoms. Such distances can be estimated by performing experiments of Nuclear Magnetic Resonance (NMR). Unfortunately, data obtained during these experiments are usually noisy and affected by errors. In particular, some of the estimated distances can be wrong, typically because assigned to the wrong pair of atoms. When particular assumptions are satisfied, the problem can be discretized, and solved by employing an ad-hoc algorithm called Branch & Prune (BP). However, this algorithm has been proved to be less efficient than a meta-heuristic algorithm when the percentage of wrong distances is large. We propose a parallel version of the BP algorithm which is able to handle this kind of instances. The scalability of the proposed algorithm allows for solving very large instances containing wrong distances. Implementation details of the algorithm in C/MPI are discussed, and computational experiments, performed on the nation-wide grid infrastructure Grid5000, are presented.