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Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction

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  • Marek Śmieja
  • Dawid Warszycki

Abstract

Fingerprints, bit representations of compound chemical structure, have been widely used in cheminformatics for many years. Although fingerprints with the highest resolution display satisfactory performance in virtual screening campaigns, the presence of a relatively high number of irrelevant bits introduces noise into data and makes their application more time-consuming. In this study, we present a new method of hybrid reduced fingerprint construction, the Average Information Content Maximization algorithm (AIC-Max algorithm), which selects the most informative bits from a collection of fingerprints. This methodology, applied to the ligands of five cognate serotonin receptors (5-HT2A, 5-HT2B, 5-HT2C, 5-HT5A, 5-HT6), proved that 100 bits selected from four non-hashed fingerprints reflect almost all structural information required for a successful in silico discrimination test. A classification experiment indicated that a reduced representation is able to achieve even slightly better performance than the state-of-the-art 10-times-longer fingerprints and in a significantly shorter time.

Suggested Citation

  • Marek Śmieja & Dawid Warszycki, 2016. "Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0146666
    DOI: 10.1371/journal.pone.0146666
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    References listed on IDEAS

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    1. Marek Śmieja & Dawid Warszycki & Jacek Tabor & Andrzej J Bojarski, 2014. "Asymmetric Clustering Index in a Case Study of 5-HT1A Receptor Ligands," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-7, July.
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    Cited by:

    1. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 493-518, September.
    2. Zhen Chen & Xiaoyan Han & Chengwei Fan & Tianwen Zheng & Shengwei Mei, 2019. "A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction," Energies, MDPI, vol. 12(4), pages 1-15, February.

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