一个新的基于片段的药虚拟查工作流程识别了SARS-CoV-2 NSP13酶的强有力的抑制剂
Jordi Doijen1, Jiexiong Xie1, Simone Marsili2
1Johnson & Johnson, Beerse, Belgium.
Journal of computational chemistry
|September 5, 2025
概括
研究人员使用一种名为FragmentScout的新计算方法发现了13种潜在的抗病毒药物,针对SARS-CoV-2 NSP13螺旋酶. 这些抑制剂对开发抗病毒新疗法具有前景.
科学领域:
- 药物发现
- 计算化学
- 病毒学
背景情况:
- SARS-CoV-2 NSP13 螺旋酶是抗病毒药物开发的关键目标.
- 基于碎片的查为药物发现提供了有价值的结构数据.
- 可以改进分析碎片选数据的现有方法.
研究的目的:
- 报告SARS-CoV-2 NSP13螺旋酶的新型抑制剂的体发现.
- 为了介绍和验证新的计算工作流程,
- 为了证明FragmentScout在识别有力的抗病毒药物的有用性.
主要方法:
- 使用一种名为FragmentScout的基于片段的虚拟选工作流程.
- 采用XChem查的SARS-CoV-2 NSP13螺旋酶的公开可访问的结构数据.
- 使用LigandScout XT生成联合药物查询和搜索3D结构数据库.
主要成果:
- 发现了13种SARS-CoV-2 NSP13螺旋酶的新型微分子强抑制剂.
- 通过细胞抗病毒测定和生物物理ThermoFluor测定验证的抑制剂.
- 展示了FragmentScout能够从毫米碎片中识别微分子命中.
结论:
- FragmentScout 工作流程是基于碎片的领先发现的宝贵工具.
- 这种方法增强了结晶学断片选数据集的系统挖掘.
- 已识别的抑制剂代表了开发新型抗病毒药物的有希望的线索.
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