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Molecular similarity measures.

Gerald M Maggiora1, Veerabahu Shanmugasundaram

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

Methods in Molecular Biology (Clifton, N.J.)
|September 15, 2010
PubMed
Summary
This summary is machine-generated.

Molecular similarity analysis (MSA) is key in chemistry, especially medicinal chemistry. This work explores various molecular representations and similarity measures, discussing their strengths and impact on chemical space and activity landscapes.

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Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Cheminformatics

Background:

  • Molecular similarity is fundamental to chemical reasoning and medicinal chemistry.
  • Molecular dissimilarity is increasingly important in combinatorial chemistry and high-throughput screening.
  • The representation of molecular information is crucial for effective molecular similarity analysis (MSA).

Purpose of the Study:

  • To provide a pedagogical discussion of molecular similarity measures.
  • To explore different mathematical structures for representing molecular information.
  • To examine the relationship between molecular similarity, representation, and chemical space.

Main Methods:

  • Utilized four mathematical structures: sets, graphs, vectors, and functions for molecular representation.
  • Focused on similarity measures (coefficients/indices) as functions mapping molecular representations to numerical values.
  • Discussed the concept of chemical space induced by pairwise molecular similarity.

Main Results:

  • Presented a variety of molecular similarity measures, detailing their strengths and limitations.
  • Expanded the discussion on chemical spaces, including activity landscapes and activity cliffs.
  • Highlighted the importance of the representation problem in determining the applicability of MSA.

Conclusions:

  • Molecular similarity measures are essential tools in understanding chemical structures and their relationships.
  • The choice of molecular representation significantly influences the outcomes of similarity analysis.
  • Understanding chemical space topography, including activity cliffs, is vital for structure-activity relationship studies.