  Structure-based virtual screening is an important tool in early stage drug
discovery that scores the interactions between a target protein and candidate
ligands. As virtual libraries continue to grow (in excess of $10^8$ molecules),
so too do the resources necessary to conduct exhaustive virtual screening
campaigns on these libraries. However, Bayesian optimization techniques can aid
in their exploration: a surrogate structure-property relationship model trained
on the predicted affinities of a subset of the library can be applied to the
remaining library members, allowing the least promising compounds to be
excluded from evaluation. In this study, we assess various surrogate model
architectures, acquisition functions, and acquisition batch sizes as applied to
several protein-ligand docking datasets and observe significant reductions in
computational costs, even when using a greedy acquisition strategy; for
example, 87.9% of the top-50000 ligands can be found after testing only 2.4% of
a 100M member library. Such model-guided searches mitigate the increasing
computational costs of screening increasingly large virtual libraries and can
accelerate high-throughput virtual screening campaigns with applications beyond
docking.
