基于蛋白质结构的有机化学驱动的干设计从超大化学空间
François Sindt1, Anthony Seyller1, Merveille Eguida1
1Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France.
ACS central science
|April 1, 2024
概括
大规模化学图书馆的计算选识别了潜在的药物候选者. 这种基于结构的方法快速浏览广的化学空间,优先考虑新型和可合成的化合物用于药物发现.
科学领域:
- 计算化学和药物发现
- 药品化学和药理学 药品化学和药理学
背景情况:
- 超大化学空间 (数十亿个化合物) 对于药物发现中的成功识别至关重要.
- 计算和导航这些广的空间需要专门的计算工具.
- 现有的方法在有效选和优先考虑化合物方面面临挑战.
研究的目的:
- 开发和验证一种基于结构的计算方法来选超大化学空间.
- 在三维目标约束中列出类似药物的化合物.
- 识别用于制药目标的新型和可合成的命中化合物.
主要方法:
- 商业化学试剂被对接到感兴趣的目标.
- 接试剂使用有机化学和拓规则连接起来,以列举化合物.
- 该方法应用于雌激素β受体和多巴胺D3受体点.
主要成果:
- 计算方法快速列出已知的配体和新的化学候选物.
- 通过体外结合测定,通过实验证实了确定的匹配结果.
- 该方法在不同大小和复杂性的化学空间中证明了效率.
结论:
- 提出的基于结构的选方法有效地导航超大化学空间.
- 它优先考虑容易合成和潜在的新药候选药物.
- 这种通用方法需要最小的计算资源,适用于各种对接算法.
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