Evidence map›Paper›PMID 33757582›Full record

ArticleJournal of cheminformatics2021

Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach.

Hiroyuki Kuwahara, Xin Gao

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 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

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  19. Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025
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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

2 authors.

Hiroyuki KuwaharaComputational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
Xin GaoComputational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia. xin.gao@kaust.edu.sa.ORCID http://orcid.org/0000-0002-7108-3574

Funding

King Abdullah University of Science and Technology BAS/1/1624-01King Abdullah University of Science and Technology FCC/1/1976-18King Abdullah University of Science and Technology FCC/1/1976-23King Abdullah University of Science and Technology FCC/1/1976-25King Abdullah University of Science and Technology FCC/1/1976-26King Abdullah University of Science and Technology FCS/1/4102-02King Abdullah University of Science and Technology URF/1/3412-01King Abdullah University of Science and Technology URF/1/3450-01
6 · The paper itself

Abstract

Two-dimensional (2D) chemical fingerprints are widely used as binary features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we identified 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related ones and examined the degree to which these related 2D fingerprints influenced molecular similarity scores calculated with the Tanimoto coefficient. Our analysis identified many related fingerprints in publicly available fingerprint schemes and showed that their presence in the feature set could have substantial effects on the similarity scores and bias the outcome of molecular similarity analysis. Our results have implication in the optimal selection of 2D fingerprints for compound similarity analysis and the identification of potential hits for compounds with target biological activity in VS.

Indexed as

2D fingerprintChemoinformaticsSimilarity-based virtual screeningStructure-activity relationshipUnsupervised feature selection

Identifiers

PMID33757582
PMCPMC7989080

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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.