Literature DB >> 27485978

The Calculation of Molecular Structural Similarity: Principles and Practice.

Peter Willett1.   

Abstract

Measures of structural similarity play an important role in chemoinformatics for applications such as similarity searching, database clustering and molecular diversity analysis. A similarity measure comprises three components: a structure representation; a weighting scheme; and a similarity coefficient. The paper introduces these components and describes methods for comparing different measures. The use of similarity measures in chemoinformatics research is illustrated by recent projects in the author's laboratory on: the interactions between a weighting scheme and a similarity coefficient; the design of comparative studies of similarity measures; the use of 2D fingerprints for scaffold-hopping searches; and the registration of orphan drugs for rare diseases.
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Cluster analysis; Descriptor; Fingerprint; Molecular diversity analysis; Molecular similarity; Similarity coefficient; Similarity measure; Similarity searching; Structural similarity; Structure representation; Weighting scheme

Year:  2014        PMID: 27485978     DOI: 10.1002/minf.201400024

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  19 in total

1.  Do We Build Similar Molecules for Comorbid Diseases? Tevarud in Drug Design, an Analysis for Depression and Inflammation.

Authors:  F Esra Önen Bayram; Sarah A A Alradhwani; Gulcin Tugcu; Hande Sipahi
Journal:  ACS Med Chem Lett       Date:  2020-01-16       Impact factor: 4.345

2.  Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure.

Authors:  Ryo Kunimoto; Martin Vogt; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2016-08-11       Impact factor: 3.686

3.  Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation.

Authors:  Christian Feldmann; Jürgen Bajorath
Journal:  iScience       Date:  2022-08-27

4.  Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.

Authors:  Richard K Belew; Stefano Forli; David S Goodsell; T J O'Donnell; Arthur J Olson
Journal:  J Chem Inf Model       Date:  2016-07-25       Impact factor: 4.956

5.  A palette of fluorophores that are differentially accumulated by wild-type and mutant strains of Escherichia coli: surrogate ligands for profiling bacterial membrane transporters.

Authors:  Jesus Enrique Salcedo-Sora; Srijan Jindal; Steve O'Hagan; Douglas B Kell
Journal:  Microbiology (Reading)       Date:  2021-02       Impact factor: 2.777

6.  MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites.

Authors:  Steve O'Hagan; Douglas B Kell
Journal:  Front Pharmacol       Date:  2016-08-22       Impact factor: 5.810

Review 7.  Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science.

Authors:  Piotr Minkiewicz; Małgorzata Darewicz; Anna Iwaniak; Justyna Bucholska; Piotr Starowicz; Emilia Czyrko
Journal:  Int J Mol Sci       Date:  2016-12-06       Impact factor: 5.923

8.  Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures.

Authors:  Steve O'Hagan; Douglas B Kell
Journal:  J Cheminform       Date:  2017-03-09       Impact factor: 5.514

9.  Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

Authors:  Antonio Peón; Stefan Naulaerts; Pedro J Ballester
Journal:  Sci Rep       Date:  2017-06-19       Impact factor: 4.379

10.  Novel Approach to Classify Plants Based on Metabolite-Content Similarity.

Authors:  Kang Liu; Azian Azamimi Abdullah; Ming Huang; Takaaki Nishioka; Md Altaf-Ul-Amin; Shigehiko Kanaya
Journal:  Biomed Res Int       Date:  2017-01-09       Impact factor: 3.411

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