快速探索分子图的组装化学空间
Ian Seet1, Keith Y Patarroyo1, Gage Siebert2
1School of Chemistry, University of Glasgow, Glasgow G12 8QQ, U.K.
Journal of chemical information and modeling
|December 7, 2025
概括
我们开发了一种快速,准确的算法来计算分子组合指数 (MA),这是衡量分子复杂性的指标. 这种新方法使得大规模的化学空间探索和分析能够用于药物发现和生物标记检测.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 量化分子复杂性对于生物特征检测和化学信息学等应用至关重要.
- 计算分子组合指数 (MA) 的现有方法是计算密集的,对于大型数据集来说是不可扩展的.
研究的目的:
- 介绍一个精确且可扩展的算法来计算分子组合指数 (MA).
- 引入一个新的指标,联合集会重叠 (JAO),用于评估全球脚手架在化学空间的重复使用.
主要方法:
- 开发了一个精确的,可扩展的算法,使用动态编程和分支和边界启发式来计算MA.
- 优先考虑最大的重复子图,并使用一组边缘列表来表示碎片化.
- 计算的MAs为300k COCONUT自然产品和配置的计算性能.
- 引入了基于联合装配空间的联合装配重叠 (JAO) 度量.
主要成果:
- 新的算法高达6个数量级的速度比以前的方法更快的分子>500Da.
- 在之前的算法由于时间限制而失败的情况下,实现了精确的MA计算.
- 证明JAO提供了与Tanimoto相似性和MCS相比不同的化学空间排名,特别是对于类固醇和.
- 成功计算了自然产品大量数据集的MA.
结论:
- 开发的算法使分子组合指数成为大规模化学空间分析的实用工具.
- 雅奥指标提供了一种评估基层结构相似性的新方法,强调全球脚手架的再利用.
- 这些进展促进了对化学多样性的更深入的探索,用于各种科学应用.
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