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A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening
journal contribution
posted on 2019-10-17, 21:13 authored by Maritza Hernandez, Guo Liang Gan, Kirby Linvill, Carl Dukatz, Jun Feng, Govinda BhisettiMeasuring 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.