用扩展的相似度指数采样和绘制化学空间
Kenneth López-Pérez1, Edgar López-López2,3, José L Medina-Franco2
1Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32611, USA.
Molecules (Basel, Switzerland)
|September 9, 2023
概括
化学地图 (ChemMaps) 使用卫星化合物和主要成分分析可视化大型化学数据集. 扩展的相似性指数通过识别相关的抽样区域并减少数据维度以改善化学空间导航来改进这种方法.
科学领域:
- 化学 化学 化学
- 化学信息学 化学信息学
- 计算化学计算化学
背景情况:
- 化学空间可视化有助于复合图书馆设计,多样性分析和结构-属性关系探索.
- 在药物发现和天然产品研究中,应用非常突出.
- 导航大型化学数据集需要近似由于高维度.
研究的目的:
- 介绍ChemMaps,这是一个可视化方法,用于近似大型化学数据集.
- 为了证明扩展的相似性指数如何可以增强ChemMaps.
- 为了减少描述化学图书馆所需的高维数据.
主要方法:
- 化学地图 (ChemMaps) 通过使用选定的卫星化合物近似测量化学物质的空间分布.
- 在相似性矩阵上的主要组件分析是在选定的卫星上进行的.
- 扩展的相似性指数用于确定相关的抽样区域.
主要成果:
- 化学地图有效地接近大型化学数据集的分布.
- 扩展的相似性指数有助于选择信息卫星化合物.
- 该方法减少了化学图书馆数据的维度.
结论:
- 化学地图为导航和理解大型化学空间提供了一种可行的方法.
- 扩展的相似性指数为ChemMaps中的卫星选择提供了改进.
- 这种方法有助于更有效地探索化学库.
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