Kathryn E Kirchoff, James Wellnitz, Joshua E Hochuli, Travis Maxfield, Konstantin I Popov, Shawn Gomez, Alexander Tropsha
{"title":"Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search.","authors":"Kathryn E Kirchoff, James Wellnitz, Joshua E Hochuli, Travis Maxfield, Konstantin I Popov, Shawn Gomez, Alexander Tropsha","doi":"10.1007/978-3-031-56060-6_3","DOIUrl":"https://doi.org/10.1007/978-3-031-56060-6_3","url":null,"abstract":"<p><p>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 <i>k</i>-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.</p>","PeriodicalId":519896,"journal":{"name":"Advances in information retrieval : ... European Conference on IR Research, ECIR ... proceedings. European Conference on IR Research","volume":"14609 ","pages":"34-49"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10998712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140871357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}