# 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  
**arXiv:** [2402.07970](https://arxiv.org/abs/2402.07970)  
**Date:** 2024-02-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.07970v1/

## Key points

- Nearest neighbor-based similarity searching in chemistry is crucial for applications like drug discovery.
- Traditional brute-force methods for this task can be computationally expensive and time-consuming due to the size of chemical databases.
- Combining low-dimensional chemical embeddings with a k-d tree data structure can achieve rapid nearest neighbor queries while maintaining high performance on standard benchmarks.
- SmallSA, a learned embedding generated using the SALSA framework trained on 1.4 million chemical compounds, significantly accelerates searches on large databases, executing queries on over one billion chemicals in less than a second on a single CPU core – five orders of magnitude faster than brute-force methods.
- SmallSA achieves competitive performance on standard chemical similarity benchmarks compared to traditional fingerprinting methods like ECFPs and ECFCs, highlighting the potential of leveraging low-dimensional embeddings and k-d trees for efficient chemical similarity searching in drug discovery and other chemistry applications.

## Summary

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In the field of chemistry, nearest neighbor-based similarity searching is a crucial task, particularly in applications like drug discovery. Traditional approaches to this task often rely on brute-force methods, which can be computationally expensive and time-consuming due to the vast size of modern chemical databases. Previous advancements in computational techniques for this task have been limited in their generalizability and efficiency. In this study, we explore the potential of combining low-dimensional chemical embeddings with a k-d tree data structure to achieve rapid nearest neighbor queries while maintaining high performance on standard chemical similarity benchmarks. Our approach addresses challenges faced by existing tools such as ChemMine and FPSim2, which utilize partitioning schemes based on Baldi bounds but are limited by binary embeddings and unequal partition sizes. To overcome these limitations, we propose SmallSA, a learned embedding generated using the SALSA framework trained on 1.4 million chemical compounds from the Enamine catalog. We compare SmallSA with commonly used Extended-Connectivity Fingerprints (ECFPs) and Extended-Connectivity Count Vectors (ECFCs) as well as their low-dimensional versions. Our results demonstrate that our approach significantly accelerates searches on large chemical databases, executing queries on over one billion chemicals in less than a second on a single CPU core – five orders of magnitude faster than brute-force methods. Furthermore, SmallSA achieves competitive performance on standard chemical similarity benchmarks compared to traditional fingerprinting methods like ECFPs and ECFCs. This highlights the potential of leveraging low-dimensional embeddings and k-d trees for efficient chemical similarity searching in drug discovery and other applications within the field of chemistry.