J Chem Inf Model. 2020 Dec 28;60(12):6167-6184. doi: 10.1021/acs.jcim.0c00393. Epub 2020 Oct 23.
Structurally similar analogues of given query compounds can be rapidly retrieved from chemical databases by the molecular similarity search approaches. However, the computational cost associated with the exhaustive similarity search of a large compound database will be quite high. Although the latest indexing algorithms can greatly speed up the search process, they cannot be readily applicable to molecular similarity search problems due to the lack of Tanimoto similarity metric implementation. In this paper, we first implement Python or C++ codes to enable the Tanimoto similarity search via several recent indexing algorithms, such as Hnsw and Onng. Moreover, there are increasing interests in computational communities to develop robust benchmarking systems to access the performance of various computational algorithms. Here, we provide a benchmark to evaluate the molecular similarity searching performance of these recent indexing algorithms. To avoid the potential package dependency issues, two separate benchmarks are built based on currently popular container technologies, Docker and Singularity. The Singularity container is a rather new container framework specifically designed for the high-performance computing (HPC) platform and does not need the privileged permissions or the separated daemon process. Both benchmarking methods are extensible to incorporate other new indexing algorithms, benchmarking data sets, and different customized parameter settings. Our results demonstrate that the graph-based methods, such as Hnsw and Onng, consistently achieve the best trade-off between searching effectiveness and searching efficiencies. The source code of the entire benchmark systems can be downloaded from https://github.uconn.edu/mldrugdiscovery/MssBenchmark.
通过分子相似性搜索方法，可以从化学数据库中快速检索到与给定查询化合物结构相似的类似物。然而，对大型化合物数据库进行穷举相似性搜索的计算成本将会非常高。尽管最新的索引算法可以大大加快搜索过程，但由于缺乏 Tanimoto 相似性度量的实现，它们不能直接应用于分子相似性搜索问题。在本文中，我们首先实现了 Python 或 C++代码，以通过几种最近的索引算法（如 Hnsw 和 Onng）实现 Tanimoto 相似性搜索。此外，计算社区越来越有兴趣开发强大的基准测试系统，以评估各种计算算法的性能。在这里，我们提供了一个基准来评估这些最近的索引算法的分子相似性搜索性能。为了避免潜在的软件包依赖问题，我们基于当前流行的容器技术（Docker 和 Singularity）分别构建了两个基准。Singularity 容器是一个专门为高性能计算（HPC）平台设计的新型容器框架，不需要特权权限或单独的守护进程。这两种基准测试方法都可以扩展到包含其他新的索引算法、基准测试数据集和不同的定制参数设置。我们的结果表明，基于图的方法，如 Hnsw 和 Onng，在搜索效果和搜索效率之间始终能达到最佳的权衡。整个基准系统的源代码可以从 https://github.uconn.edu/mldrugdiscovery/MssBenchmark 下载。
