BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space
The rapid expansion of chemical space presents significant challenges in identifying novel ligands for drug targets. Here, we introduce BIOPTIC B1, an ultra-high-throughput ligand-based virtual screening system capable of rapidly evaluating multi-billion-molecule libraries in minutes. In retrospective analyses across seven diverse drug targets, BIOPTIC B1 demonstrated performance comparable to state-of-the-art machine learning models, including Chemprop and Gradient Boosting. Prospectively, we applied BIOPTIC B1 to leucine-rich repeat kinase 2 (LRRK2), a high-priority drug target for Parkinson's disease. BIOPTIC B1 successfully identified multiple novel ligands binding both wild-type and G2019S-mutant LRRK2 with dissociation constants (K<sub>d</sub>) as low as 110 nM, directly from the Enamine REAL Space of 40 billion molecules. These findings highlight BIOPTIC B1 as a powerful tool for novel hit identification and scaffold hopping within ultra-large chemical space, offering significant advancements in virtual screening for drug discovery.
Authors
- Simon Steshin
- В. М. Виноградов
- Ivan Izmailov
- Kong T. Nguyen
- Andrey Doronichev
Institutions
- Electron Optica (United States) (US)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2025-09-12
- DOI
- https://doi.org/10.1021/acs.jcim.5c00743
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50