The Molecular Distance Geometry Problem
(MDGP) is the one of finding molecular conformations
that satisfy a set of distance constraints obtained through
experimental techniques such as Nuclear Magnetic Resonance
(NMR). We consider a subclass of MDGP instances that can be
discretized, where the search domain has the structure of a tree,
which can be explored by using an *interval* Branch & Prune
(*i*BP) algorithm. When all available distances are exact, all
candidate positions for a given molecular conformation can be
enumerated. This is however not possible in presence of interval
distances, because a continuous subset of positions can actually
be computed for some atoms. The focus of this work is on a
new scheme for an adaptive generation of a discrete subset of
candidate positions from this continuous subset. Our generated
candidate positions do not only satisfy the distances employed
in the discretization process, but also additional distances that
might be available (the so-called pruning distances). Therefore,
this new scheme is able to guide more efficiently the search
in the feasible regions of the search domain. In this work,
we motivate the development and formally introduce this new
adaptive scheme. Presented computational experiments show
that *i*BP, integrated with our new scheme, outperforms the
standard *i*BP on a set of NMR-like instances.