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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space

Simon Steshin, В. М. Виноградов, Ivan Izmailov, Kong T. Nguyen et al.
1 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
1.50
article

BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space

Simon Steshin, В. М. Виноградов, Ivan Izmailov, Kong T. Nguyen, Andrey Doronichev
article en
1 citations

Abstract

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.

Journal of Chemical Information and ModelingVol. 65(19)
Electron Optica (United States) (US)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space — Simon Steshin, В. М. Виноградов, et al. · Journal of Chemical Information and Modeling (2025) | TGRS Research Map | TGRS