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Chemoinformatics and Computational Chemical Biology pp 39–100Cite as

Molecular Similarity Measures

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 672))

Abstract

Molecular similarity is a pervasive concept in chemistry. It is essential to many aspects of chemical reasoning and analysis and is perhaps the fundamental assumption underlying medicinal chemistry. Dissimilarity, the complement of similarity, also plays a major role in a growing number of applications of molecular diversity in combinatorial chemistry, high-throughput screening, and related fields. How molecular information is represented, called the representation problem, is important to the type of molecular similarity analysis (MSA) that can be carried out in any given situation. In this work, four types of mathematical structure are used to represent molecular information: sets, graphs, vectors, and functions. Molecular similarity is a pairwise relationship that induces structure into sets of molecules, giving rise to the concept of chemical space. Although all three concepts – molecular similarity, molecular representation, and chemical space – are treated in this chapter, the emphasis is on molecular similarity measures. Similarity measures, also called similarity coefficients or indices, are functions that map pairs of compatible molecular representations that are of the same mathematical form into real numbers usually, but not always, lying on the unit interval. This chapter presents a somewhat pedagogical discussion of many types of molecular similarity measures, their strengths and limitations, and their relationship to one another. An expanded account of the material on chemical spaces presented in the first edition of this book is also provided. It includes a discussion of the topography of activity landscapes and the role that activity cliffs in these landscapes play in structure–activity studies.

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Acknowledgments

The authors would like to thank Tom Doman for his constructive comments on the original version of this manuscript, and Mark Johnson, Mic Lajiness, John Van Drie, and Tudor Oprea for helpful discussions. Special thanks are given to Jurgen Bajorath and Jose Medina-Franco, for providing several figures and for their helpful comments.

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Authors and Affiliations

  1. Department of Pharmacology & Toxicology, College of Pharmacy, University of Arizona, Tucson, AZ, USA

    Gerald M. Maggiora

  2. Anti-Bacterials Computational Chemistry, Department of Structural Biology, WorldWide Medicinal Chemistry, Pfizer Global Research & Development, Groton, CT, USA

    Veerabahu Shanmugasundaram

Authors
  1. Gerald M. Maggiora
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  2. Veerabahu Shanmugasundaram
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Editor information

Editors and Affiliations

  1. , Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universitä, Dahlmannstr. 2, Bonn, 53113, Germany

    Jürgen Bajorath

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© 2011 Humana Press

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Maggiora, G.M., Shanmugasundaram, V. (2011). Molecular Similarity Measures. In: Bajorath, J. (eds) Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology, vol 672. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-839-3_2

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  • DOI: https://doi.org/10.1007/978-1-60761-839-3_2

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-838-6

  • Online ISBN: 978-1-60761-839-3

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