在超过110亿个化合物的虚拟库中发现基于synthon的配体
Arman A Sadybekov1,2, Anastasiia V Sadybekov1,2, Yongfeng Liu3,4
1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
Nature
|December 16, 2021
概括
V-SYNTHES提供了一种基于结构的虚拟选的新层次方法. 这种方法可以快速识别大量化学库中的强效药物候选物,显著降低计算成本并提高命中率.
科学领域:
- 药物发现和药物化学
- 计算化学
- 化学合成
背景情况:
- 基于结构的虚拟连接体查对于早期药物发现至关重要.
- 超大的虚拟复合库需要先进的选方法.
- 现有的方法难以跟上化学图书馆的快速增长.
研究的目的:
- 介绍V-SYNTHES,一个基于synthon的分级选方法.
- 让化学图书馆能够有效地进行选.
- 为了加快新药线索的识别.
主要方法:
- V-SYNTHES采用一个等级,组合的策略.
- 它确定了最佳的脚手架-synthon组合作为起点.
- 种子的代处理选择具有高对接分数的完整分子.
主要成果:
- V-SYNTHES对超过11亿种化合物进行了选,
- 测试新型大麻素抗剂的成功率为33%,包括亚微分子配体.
- 优化的类似物显示出高强度 (Ki=0. 9nM) 和对CB2/CB1受体的选择性.
结论:
- V-SYNTHES在效率和命中率上显著超过标准的虚拟选.
- 该方法可扩展,可适应各种对接算法,并对酶标进行验证.
- 这种方法加速了大型组合图书馆的领先发现.
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