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Related Experiment Video

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Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

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Bloom filters for molecules.

Jorge Medina¹, Andrew D White²

¹Department of Chemical Engineering, University of Rochester, Rochester, NY, USA.

Journal of Cheminformatics

|October 12, 2023

Summary

This summary is machine-generated.

Bloom filters offer efficient, low-memory solutions for checking molecule presence in ultra-large chemical libraries. Using canonical SMILES strings with FNV hashing provides fast, accurate membership tests with under 1% false positives.

Keywords:

Bloom filter Fingerprint Hashing SMILES

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Area of Science:

Computational Chemistry
Cheminformatics
Data Structures

Background:

Ultra-large chemical libraries contain billions of molecules.
Efficiently searching these libraries for specific molecules is a significant challenge.

Purpose of the Study:

To investigate the use of Bloom filters for membership testing in large chemical libraries.
To evaluate Bloom filters using both string and fingerprint molecular representations.

Main Methods:

Bloom filters were studied for membership testing of molecules.
Canonical SMILES strings and molecular fingerprints were used as representations.
The Fowler-Noll-Voll (FNV) hashing function was employed with canonical SMILES.

Main Results:

Bloom filters can store billions of molecules in gigabytes of memory, with membership checks in milliseconds.
String representations, specifically canonical SMILES, achieved false positive rates below 1%.
String representations require significantly less storage compared to fingerprint representations.

Conclusions:

Bloom filters, particularly with canonical SMILES and FNV hashing, provide a fast, accurate, and memory-efficient method for testing molecule presence.
This approach is suitable for managing and querying ultra-large chemical datasets.
Implementations are available for checking molecule purchasability, patent status, and natural product classification.