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一个开源的药物发现平台实现了超大虚拟屏幕
Christoph Gorgulla1,2,3, Andras Boeszoermenyi4,5, Zi-Fu Wang4
1Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA, USA. cgorgulla@g.harvard.edu.
Nature
|March 11, 2020
概括
虚拟流是一个开源平台,通过有效选超过10亿种化合物来简化药物发现. 这加快了对高亲和度药物候选物的鉴定,例如iKeap1,该药物针对KEAP1和NRF2的相互作用.
科学领域:
- 计算化学和药物发现
- 生物信息学和化学信息学
- 分子建模和仿真
背景情况:
- 药物开发是漫长而昂贵的,平均需要10年和30亿美元.
- 在临床前和临床阶段的高消耗率,加上昂贵的实验室实验,阻碍了药物发现.
- 基于结构的虚拟选提供了一种有前途的方法来提高击中质量并缩短开发时间表.
研究的目的:
- 推出一个新的,自动化的,可扩展的开源平台,用于准备和选超大复合库.
- 展示平台在处理大规模数据集和多样化的对接程序方面的能力.
- 展示VirtualFlow在治疗目标中识别高亲和度结合物的潜力.
主要方法:
- 开发VirtualFlow,一个高度自动化的多功能开源平台,用于虚拟选.
- 准备一个大型的,准备装载的连接物库,超过14亿个商业可用的分子.
- 使用虚拟流和各种对接程序选超过10亿个化合物.
主要成果:
- 一个超大化合物库 (超过14亿个分子) 的成功准备和选.
- 对KEAP1具有亚微分子亲和度的结构多样化分子的鉴定.
- 发现一种抑制剂,iKeap1,具有纳米分子亲和力 (114 nM Kd),可以破坏KEAP1-NRF2的相互作用.
结论:
- 虚拟流提供了一个高效,可访问和可扩展的解决方案,用于大规模的基于结构的虚拟选.
- 该平台可以探索广的化学空间以识别高亲和度药物候选物.
- 虚拟流有潜力显著加速药物发现并降低相关成本.
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