有效地找到活动悬崖.
Akash Surendran1, Ramón Alain Miranda-Quintana1
1Department of Chemistry and Quantum Theory Project, University of Florida, FL, Gainesville, 32611.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
活动悬崖是药物设计中的一个主要障碍. 本研究引入了一种使用BitBIRCH集群算法的双重方法,以有效地识别和避免大型复合库中的这些悬崖.
科学领域:
- 计算化学和药物发现.
背景情况:
- 活动悬崖,其中小的结构变化导致大功率转移,在计算药物设计中构成重大挑战.
- 现有的机器学习方法很难有效地解决活动悬崖问题.
研究的目的:
- 为导航和分析结构-活动景观提出一种新的双重方法.
- 为了快速识别活动悬崖和最大限度地平滑的化学空间区域.
主要方法:
- 基于BitBIRCH集群算法的双重方法的开发.
- 应用算法来分析大型复合库的结构-活动关系.
主要成果:
- BitBIRCH算法有助于有效识别活动悬崖.
- 该方法允许在化学空间中发现平滑的部门,可能导致更可预测的候选药物.
- 这些方法对于非常大的数据集是可扩展的.
结论:
- 提出的双重方法为解决药物设计中的活动悬崖提供了一个强大的工具.
- 这种方法增强了对大型化学库的分析,以优化药物发现管道.
- 自由可用的代码使研究人员能够实施这些策略.
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