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May 29, 2026Journal of Chemical Information and Modeling0 citations

A Multistage Virtual Screening Strategy Integrating Molecular Similarity, Deep Learning Scoring, and Molecular Docking toward the Discovery of Novel LRRK2 Inhibitors

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XGXiaoqing GongJHJunli HuangCFChenggong Fu

Key Points

  • The central aim is to discover novel inhibitors for LRRK2, a target implicated in neurodegenerative diseases.
  • Conducted a multistage virtual screening combining molecular similarity, deep learning scoring, and molecular docking.
  • Assessed compound C-298 for cell viability and its effect on reactive oxygen species levels.
  • Utilized molecular dynamics simulations to analyze interactions influencing inhibitory activity.
  • Compound C-298 exhibited IC50 values of 315.7 nM for wild-type LRRK2 and 255.4 nM for the G2019S mutant.
  • Demonstrated comparable cytotoxicity to the control MLi-2 and effectively reduced reactive oxygen species levels.
  • Inhibition of LRRK2 phosphorylation was concentration-dependent, influenced by specific hydrogen bond interactions.

Abstract

values of 315.7 nM against wild-type LRRK2 and 255.4 nM against the G2019S mutant. The cell viability assay indicated comparable cytotoxicity of compound C-298 to the positive control MLi-2. Furthermore, compound C-298 effectively reduced reactive oxygen species levels and inhibited the phosphorylation of LRRK2 (Ser935) and Rab10 (Thr73) in a concentration-dependent manner. Molecular dynamics simulations elucidated that hydrogen bond interactions with residues Glu1948 and Ala1950, as well as molecular rigidity, play critical roles in inhibitory activity. Our study demonstrates the utility of AI-assisted virtual screening in accelerating LRRK2 inhibitor discovery and identifies compound C-298 as a promising inhibitor, providing valuable insights for further rational design of LRRK2 inhibitors.

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Cite This Study

Gong et al. (2026) studied this question.

synapsesocial.com/papers/6a192c67fab5b468c44154dfhttps://doi.org/10.1021/acs.jcim.6c00241
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