Evidence map›Paper›PMID 33892799›Full record

ArticleJournal of cheminformatics2021

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection.

Ramón Alain Miranda-Quintana, Anita Rácz, Dávid Bajusz, Károly Héberger

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

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  5. ScalingJournal of chemical information and modeling · 2026
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  9. Selector: A General Python Library for Diverse Subset Selection.Journal of chemical information and modeling · 2026
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  10. Selector: A General Python Library for Diverse Subset Selection.bioRxiv : the preprint server for biology · 2025
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  12. BitBIRCH-Lean: chemical space in the palm of your workstation.bioRxiv : the preprint server for biology · 2025
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  16. iCliff Taylor's Version: Robust and Efficient Activity Cliff Determination.Journal of chemical information and modeling · 2025
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  17. SHINE: Deterministic Many-to-Many Clustering of Molecular Pathways.Journal of chemical information and modeling · 2025
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  18. Extended Quality (eQual): Radial Threshold Clustering Based onJournal of chemical information and modeling · 2025
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  19. Article
  20. Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ramón Alain Miranda-QuintanaDepartment of Chemistry, University of Florida, Gainesville, FL, 32603, USA. quintana@chem.ufl.edu.ORCID http://orcid.org/0000-0003-2121-4449
Anita RáczPlasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary.ORCID http://orcid.org/0000-0001-8271-9841
Dávid BajuszMedicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary.ORCID http://orcid.org/0000-0003-4277-9481
Károly HébergerPlasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary. heberger.karoly@ttk.hu.ORCID http://orcid.org/0000-0003-0965-939X

Funding

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal PD134416Nemzeti Kutatási Fejlesztési és Innovációs Hivatal (HU) OTKA, contract K 134260
6 · The paper itself

Abstract

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over "traditional" pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons.

Indexed as

Computational complexityConsistencyExtended similarity indicesMolecular fingerprintsMultiple comparisonsRankingsScalingSum of ranking differences

Identifiers

PMID33892799
PMCPMC8067665

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.