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Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_495679f0cec246eeadf36f804888f255

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

About this item

Full title

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

Publisher

Switzerland: MDPI AG

Journal title

Biomolecules (Basel, Switzerland), 2022-03, Vol.12 (4), p.508

Language

English

Formats

Publication information

Publisher

Switzerland: MDPI AG

More information

Scope and Contents

Contents

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query....

Alternative Titles

Full title

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_495679f0cec246eeadf36f804888f255

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_495679f0cec246eeadf36f804888f255

Other Identifiers

ISSN

2218-273X

E-ISSN

2218-273X

DOI

10.3390/biom12040508

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