利用低维分子嵌入进行快速化学相似性搜索
Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search
AI总结：
针对化学相似性搜索中暴力搜索计算成本高的问题，提出结合低维分子嵌入与k-d树数据结构的快速搜索框架，在单核CPU上实现十亿级化学品秒级检索，较暴力搜索快五个数量级且保持基准竞争力。
摘要：
基于最近邻的相似性搜索是化学领域的一项常见任务，在药物发现中有显著的应用案例。然而，该任务一些最常用的方法仍采用暴力搜索方法。在实际应用中，由于现代化学数据库规模庞大，这会带来高昂的计算成本和过度的耗时。此前针对该任务的计算改进通常依赖于硬件升级或缺乏泛化能力的特定数据集技巧。利用较低复杂度搜索算法的方法仍相对缺乏探索。然而，许多此类算法是近似解，且在处理典型的高维化学嵌入时存在困难。在此，我们评估低维化学嵌入与 k-d 树数据结构的结合能否在保持标准化学相似性搜索基准性能的同时，实现快速的最近邻查询。我们研究了标准化学嵌入的不同降维方法，以及一种学习到的、具备结构感知能力的嵌入——SmallSA。借助该框架，在单个 CPU 核心上对超过十亿种化学物质进行搜索仅需不到一秒，比暴力搜索快五个数量级。我们还证明了 SmallSA 在化学相似性基准上达到了具有竞争力的性能。
英文摘要：
Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding -- SmallSA -- for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.
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