Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search

Abstract

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.

Selenium-Mediated (−)-Epigallocatechin-3-Gallate Dynamics via Flavanone-3-Hydroxylase Regulation of Flavonoid Biosynthesis in Fu Tea (Camellia sinensis (L.) O. Kuntze)

“…The number of layers l of each node is chosen at random, and the probability distribution decays exponentially. By combining Word2vec embedding and HNSW, high speed and accuracy are achieved, which reduces the false-negative ratio and facilitates compound identification. − …”

“…To evaluate the potential mechanism of the effects of selenium on Fu tea, we utilized an untargeted metabolomics strategy with selenium-enriched Fu tea (four final concentrations: 0.0, 0.6, 1.2, and 2.4 mg kg –1 ) (Figure ). Compound identification matching was performed by analyzing the mass spectral signature data set of selenium-enriched Fu tea obtained by UHPLC-Q-Orbitalrap HRMS–MS/MS in combination with a multimillion computer library. , Extended connectivity fingerprints of f- CHEMBL and f- NIST molecules served as inputs to neural electron–ionization mass spectrometry (NEIMS) to generate predicted spectra, which were embedded by Word2vec spectral embedding and hierarchical navigational small world (HNSW) graphs to accomplish the spectral matching with high speed and high accuracy. , …”

“…28,29 Extended connectivity fingerprints of f-CHEMBL and f-NIST molecules served as inputs to neural electron− ionization mass spectrometry (NEIMS) to generate predicted spectra, which were embedded by Word2vec spectral embedding and hierarchical navigational small world (HNSW) graphs to accomplish the spectral matching with high speed and high accuracy. 28,30 Word2vec algorithm principle:…”

Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment

“…A notable advancement in this domain has been the widespread adoption of learning-based embedding models, which leverage high-dimensional vector representations to enable effective and efficient analysis and search of unstructured data [37,61]. High-dimensional Vector Similarity Search (HVSS) is a critical challenge in many domains, such as databases [25,68], information retrieval [28,32], recommendation systems [19,54], scientific computing [51,78], and large language models (LLMs) [7,12,44]. The computational complexity associated with exact query answering in HVSS has spurred recent research efforts toward developing approximate search methods [25,49,68].…”