ArticleJournal of chemical information and modeling2024
Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP.ACS central science · 2026Article
- Combining AlphaFold with Focused Virtual Library Design in the Development of Novel CCR2 and CCR5 Antagonists.Journal of chemical information and modeling · 2025Article
- A Database for Large-Scale Docking and Experimental Results.Journal of chemical information and modeling · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
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.
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Registered trials
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