  Gaussian Boson Samplers are photonic quantum devices with the potential to
perform tasks that are intractable for classical systems. As with other
near-term quantum technologies, an outstanding challenge is to identify
specific problems of practical interest where these quantum devices can prove
useful. Here we show that Gaussian Boson Samplers can be used to predict
molecular docking configurations: the spatial orientations that molecules
assume when they bind to larger proteins. Molecular docking is a central
problem for pharmaceutical drug design, where docking configurations must be
predicted for large numbers of candidate molecules. We develop a
vertex-weighted binding interaction graph approach, where the molecular docking
problem is reduced to finding the maximum weighted clique in a graph. We show
that Gaussian Boson Samplers can be programmed to sample large-weight cliques,
i.e., stable docking configurations, with high probability, even in the
presence of photon loss. We also describe how outputs from the device can be
used to enhance the performance of classical algorithms and increase their
success rate of finding the molecular binding pose. To benchmark our approach,
we predict the binding mode of a small molecule ligand to the tumor necrosis
factor-${\alpha}$ converting enzyme, a target linked to immune system diseases
and cancer.
