  Measuring similarity between molecules is an important part of virtual
screening (VS) experiments deployed during the early stages of drug discovery.
Most widely used methods for evaluating the similarity of molecules use
molecular fingerprints to encode structural information. While similarity
methods using fingerprint encodings are efficient, they do not consider all the
relevant aspects of molecular structure. In this paper, we describe a
quantum-inspired graph-based molecular similarity (GMS) method for ligand-based
VS. The GMS method is formulated as a quadratic unconstrained binary
optimization problem that can be solved using a quantum annealer, providing the
opportunity to take advantage of this nascent and potentially groundbreaking
technology. In this study, we consider various features relevant to
ligand-based VS, such as pharmacophore features and three-dimensional atomic
coordinates, and include them in the GMS method. We evaluate this approach on
various datasets from the DUD_LIB_VS_1.0 library. Our results show that using
three-dimensional atomic coordinates as features for comparison yields higher
early enrichment values. In addition, we evaluate the performance of the GMS
method against conventional fingerprint approaches. The results demonstrate
that the GMS method outperforms fingerprint methods for most of the datasets,
presenting a new alternative in ligand-based VS with the potential for future
enhancement.
