在虚拟选中使用宏分子电子密度来改善活性化合物的丰富
Wenzhi Ma1, Wei Zhang2,3, Yuan Le1
1Beijing StoneWise Technology Co Ltd., Haidian Street #15, Haidian District, 100080, Beijing, China.
Communications chemistry
|August 23, 2023
概括
本研究介绍了一种基于电子密度 (ED) 的实验方法,以提高虚拟选的准确性. 新的方法ExptGMS可以将活性化合物的识别提高20%以上.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 结构生物学是结构生物学.
背景情况:
- 虚拟选 (VS) 算法对于药物发现至关重要,但通常受到稀缺的培训数据的限制.
- 实验电子密度 (ED) 数据为结合口袋中的分子相互作用提供了详细的视图.
- 开发新的方法来利用ED数据可以克服当前VS方法的局限性.
研究的目的:
- 使用实验电子密度数据开发和验证一种新的虚拟选评分功能.
- 在虚拟选活动中改进活性化合物的丰富.
- 为研究人员提供一个实用的工具,以加强药物发现工作.
主要方法:
- 基于ED的实验电网匹配得分 (ExptGMS) 算法的开发.
- 通过将它们的形状与多分辨率实验ED网格相匹配来评分化合物.
- 使用in silico (有用诱增强数据集目录) 和湿实验室 (Covid-19 3CLpro 抑制剂) 实验的验证.
- 创建一个实验ED网格的在线数据库,用于超过17000种蛋白质.
主要成果:
- 在虚拟查中,ExptGMS显著提高了约20%的活性化合物丰富度.
- 该方法成功识别了Covid-19的四种活性抑制剂3CLpro.
- 鉴定到的最强大的抑制剂的IC50值为1.9μM.
- 建立了一个可访问的实验ED网的在线数据库.
结论:
- 实验电子密度数据可以有效地提高虚拟选性能.
- 在计算药物发现方面,ExptGMS代表了一个有前途的进步.
- 开发的数据库有助于在研究中更广泛地应用基于ED的方法.
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