使用低维分子嵌入式进行快速化学相似性搜索
Kathryn E Kirchoff1, James Wellnitz2, Joshua E Hochuli2
1Department of Computer Science, UNC Chapel Hill.
概括
这项研究引入了使用低维嵌入和k-d树进行化学相似性搜索的更快方法. 这种方法显著加快了对大型化学数据库的搜索速度,使药物发现更加有效.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 最接近邻居的搜索在化学中至关重要,特别是在药物发现中.
- 对于大型化学数据库来说,目前的粗暴强力方法在计算上昂贵.
- 现有的进步往往依赖于硬件或数据集特定的解决方案,缺乏通用性.
研究的目的:
- 为了评估与k-d树相结合的低维化学嵌入是否可以快速实现最近邻居查询.
- 评估这种方法在标准化学相似性搜索基准上的表现.
- 探索化学嵌入的维度减少和一种新的学习嵌入 (SmallSA).
主要方法:
- 使用低维的化学嵌入物.
- 实施k-d树数据结构以实现高效的搜索.
- 比较不同的尺寸缩小技术和SmallSA嵌入.
主要成果:
- 在单个CPU内核上,在不到一秒钟的时间内完成了超过10亿种化学品的搜索.
- 与粗暴武力方法相比,实现了五个数量级的加速度.
- 小SA嵌入显示了在化学相似性基准上具有竞争力的性能.
结论:
- 低维嵌入和k-d树的组合为化学相似性搜索提供了高效的解决方案.
- 这一框架显著加速了大规模的化学数据库查询.
- 开发的方法在标准基准上保持了高性能,推进了计算化学工具.
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