We present a way of exploiting partial separability in particular global optimization problems. The aim is to reduce significantly the dimension of the search space. The procedure relies on graph theory tools such as graph partitioning with node separators. We illustrate the idea on a distance geometry problem which arise in the interpretation of nuclear magnetic resonance data and in the determination of protein structures.