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Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening

Authors
  • Gladysz, Rafaela1
  • Dos Santos, Fabio Mendes1
  • Langenaeker, Wilfried2
  • Thijs, Gert3
  • Augustyns, Koen1
  • De Winter, Hans1
  • 1 Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, Campus Drie Eiken, Building A, Universiteitsplein 1, Antwerp, 2610, Belgium , Antwerp (Belgium)
  • 2 Faculty of Science, Department of Chemistry, Campus Diepenbeek, Agoralaan, Building D, Diepenbeek, 3590, Belgium , Diepenbeek (Belgium)
  • 3 Agilent, Clinical Applications Division, Technologielaan 3, Louvain, 3001, Belgium , Louvain (Belgium)
Type
Published Article
Journal
Journal of Cheminformatics
Publisher
Springer International Publishing
Publication Date
Mar 07, 2018
Volume
10
Issue
1
Identifiers
DOI: 10.1186/s13321-018-0268-9
Source
Springer Nature
Keywords
License
Green

Abstract

Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990’s by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure–activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.

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