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Updated: Jul 10, 2026
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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval.
Pierre Baldi1, Ryan W Benz, Daniel S Hirschberg
1Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California-Irvine, Irvine, CA 92697-3435, USA. pfbaldi@ics.uci.edu
Journal of Chemical Information and Modeling
|October 31, 2007
Summary
This study introduces lossless compression for chemical fingerprints, reducing storage from over 1024 bits to ~300 bits per molecule. This enables exact similarity computations and improves retrieval performance for drug discovery.
Area of Science:
- Chemoinformatics
- Computational Chemistry
- Data Compression
Background:
- Modern chemoinformatics utilizes large fingerprint vectors for small molecules, often compressed lossily.
- Existing lossy compression methods can lead to information loss and affect similarity calculations.
Purpose of the Study:
- To develop efficient, lossless compression algorithms for chemical fingerprints.
- To enable exact similarity computations from compressed molecular representations.
- To improve retrieval performance in chemoinformatics databases.
Main Methods:
- Combined statistical fingerprint models with integer entropy codes (Golomb, Elias).
- Developed new lossless compression algorithms: monotone value (MOV) coding and monotone length (MOL) coding.
- Applied MOL Elias Gamma code to encode run lengths of reordered fingerprint components.
Main Results:
- Achieved lossless compression of chemical fingerprints to ~300 bits per molecule, nearing the Shannon entropy limit.
- Demonstrated that uncompressed similarity (Tanimoto) can be computed exactly from compressed data.
- Showcased significant retrieval performance improvements on six benchmark datasets of druglike molecules.
Conclusions:
- Lossless compression of chemical fingerprints is feasible and offers substantial storage reduction.
- The developed algorithms (MOV, MOL) provide efficient compression with modest computational cost.
- Exact similarity calculations from compressed data enhance chemoinformatics database retrieval efficiency.
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