Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search

Abstract

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.

Keywords: Cheminformatics; Drug discovery; Virtual screening.

Fig.1:

Overview of the similarity search framework. k-d trees are combined with with low-dimensional chemical embeddings to produce a partitioned chemical space, which can be quickly queried for nearest neighbors.

Fig.2:

The average approximate graph edit distance (GED) between query molecule and nearest neighbors, per method, shown as a function of the number of neighbors considered. Lower distances are better. Lines are smoothed using a running average approach for simplicity of analysis.

Fig.3:

AUROC achieved by each embedding on the RDKit virtual screening benchmark of 69 query targets, grouped by target database. Each database is indicated on the x-axis. Note that out of the 69 targets, most targets (50) belong to the ChEMBL database.

Fig.4:

Example of a query molecule and the hits obtained by select high-performing embeddings.