Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds

Make-on-demand chemical libraries have drastically increased
the
reach of molecular docking, with the enumerated ready-to-dock ZINC-22
library approaching 6.4 billion molecules (July 2024). While ever-growing
libraries result in better-scoring molecules, the computational resources
required to dock all of ZINC-22 make this endeavor infeasible for
most. Here, we organize and traverse chemical space with hierarchical
navigable small-world graphs, a method we term retrieval augmented
docking (RAD). RAD recovers most virtual actives, despite docking
only a fraction of the library. Furthermore, RAD is protein-agnostic,
supporting additional docking campaigns without additional computational
overhead. In depth, we assess RAD on published large-scale docking
campaigns against D4 and AmpC spanning 99.5 million and 138 million
molecules, respectively. RAD recovers 95% of DOCK virtual actives
for both targets after evaluating only 10% of the libraries. In breadth,
RAD shows widespread applicability against 43 DUDE-Z proteins, evaluating
50.3 million associations. On average, RAD recovers 87% of virtual
actives while docking 10% of the library without sacrificing chemical
diversity.