  Fragment-based shape signature techniques have proven to be powerful tools
for computer-aided drug design. They allow scientists to search for target
molecules with some similarity to a known active compound. They do not require
reference to the full underlying chemical structure, which is essential to deal
with chemical databases containing millions of compounds. However, finding the
optimal match of a part of the fragmented compound can be time-consuming. In
this paper, we use constraint programming to solve this specific problem. It
involves finding a weighted assignment of fragments subject to connectivity
constraints. Our experiments demonstrate the practical relevance of our
approach and open new perspectives, including generating multiple, diverse
solutions. Our approach constitutes an original use of a constraint solver in a
real time setting, where propagation allows to avoid an enumeration of weighted
paths. The model must remain robust to the addition of constraints making some
instances not tractable. This particular context requires the use of unusual
criteria for the choice of the model: lightweight, standard propagation
algorithms, data structures without prohibitive constant cost. The objective is
not to design new, complex algorithms to solve difficult instances.
