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Molecular Similarity Concepts for Informatics Applications

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

The assessment of small molecule similarity is a central task in chemoinformatics and medicinal chemistry. A variety of molecular representations and metrics are applied to computationally evaluate and quantify molecular similarity. A critically important aspect of molecular similarity analysis in chemoinformatics and pharmaceutical research is that one is typically not interested in quantifying the degree of structural or chemical similarity between compounds per se, but rather in extrapolating from molecular similarity to property similarity. In other words, one assumes that there is a correlation between calculated similarity and specific properties of small molecules including, first and foremost, biological activities. Although similarity is a priori a subjective concept, and difficult to quantify, it must computationally be assessed in a formally consistent manner. Otherwise, there is little utility of similarity calculations. Consistent treatment requires approximations to be made and the consideration of alternative computational similarity concepts, as discussed herein.

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References

  1. Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204–3218

    Article  CAS  PubMed  Google Scholar 

  2. Auer J, Bajorath J (2008) Molecular similarity concepts and search calculations. Methods Mol Biol 453:327–347

    Article  CAS  PubMed  Google Scholar 

  3. Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204

    Article  CAS  PubMed  Google Scholar 

  4. Kubinyi H (1998) Similarity and dissimilarity: a medicinal chemist’s view. Perspect Drug Discov Des 9–11:225–232

    Article  Google Scholar 

  5. Lajiness MS, Maggiora GM, Shanmugasundaram V (2004) Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J Med Chem 47:4891–4896

    Article  CAS  PubMed  Google Scholar 

  6. Barnard JM (1993) Substructure searching methods. Old and new. J Chem Inf Comput Sci 33:532–538

    Article  CAS  Google Scholar 

  7. Willett P (1999) Dissimilarity-based algorithms for selecting structurally diverse sets of compounds. J Comput Biol 6:447–457

    Article  CAS  PubMed  Google Scholar 

  8. Martin YC (2001) Diverse viewpoints on computational aspects of molecular diversity. J Comb Chem 3:231–250

    Article  CAS  PubMed  Google Scholar 

  9. Bajorath J (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41:233–245

    Article  CAS  PubMed  Google Scholar 

  10. Stahura FL, Bajorath J (2003) Partitioning methods for the identification of active molecules. Curr Med Chem 10:707–715

    Article  CAS  PubMed  Google Scholar 

  11. MACCS Structural Keys; Accelrys: San Diego, CA

    Google Scholar 

  12. James CA, Weininger D. Daylight theory manual. Daylight Chemical Information Systems, Inc., Irvine, CA

    Google Scholar 

  13. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754

    Article  CAS  PubMed  Google Scholar 

  14. Good AC, Richards WG (1998) Explicit calculation of 3D molecular similarity. Perspect Drug Discov Des 9–11:321–338

    Article  Google Scholar 

  15. Rush TS, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3D scaffold hopping method and its application to a bacterial protein–protein interaction. J Med Chem 48:1489–1495

    Article  CAS  PubMed  Google Scholar 

  16. Bradley EK, Beroza P, Penzotti JE, Grootenhuis PDJ, Spellmeyer DC, Miller JL (2000) A rapid computational method for lead evolution: description and application to alpha(1)-adrenergic antagonists. J Med Chem 43:2770–2774

    Article  CAS  PubMed  Google Scholar 

  17. Gund P (1977) Three-dimensional pharmacophore pattern searching. In: Hahn FE (ed) Progress in molecular and subcellular biology, vol 5. Springer, Berlin, pp 117–142

    Chapter  Google Scholar 

  18. Johnson MA, Maggiora GM (eds) (1990) Concepts and applications of molecular similarity. Wiley, New York

    Google Scholar 

  19. Stumpfe D, Bajorath J (2011) Similarity searching. Wiley Interdiscip Rev Comput Mol Sci 1:260–282

    Article  CAS  Google Scholar 

  20. Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996

    Article  CAS  Google Scholar 

  21. Maggiora GM, Shanmugasundaram V (2004) Molecular similarity measures. Methods Mol Biol 275:1–50

    Article  CAS  PubMed  Google Scholar 

  22. Tanimoto TT (1957) IBM internal report. Nov 17

    Google Scholar 

  23. Tversky A (1977) Features of similarity. Psychol Rev 84:327–352

    Article  Google Scholar 

  24. Fligner M, Verducci J, Blower PA (2002) Modification of the Jaccard-Tanimoto similarity index for diverse selection of chemical compounds using binary strings. Technometrics 44:110–119

    Article  Google Scholar 

  25. Nisius B, Bajorath J (2010) Rendering conventional molecular fingerprints for virtual screening independent of molecular complexity and size effects. ChemMedChem 5:859–868

    Article  CAS  PubMed  Google Scholar 

  26. Schuffenhauer A, Floersheim P, Acklin P, Jacoby E (2003) Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci 43:391–405

    Article  CAS  PubMed  Google Scholar 

  27. Vogt M, Stumpfe D, Geppert H, Bajorath J (2010) Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening. J Med Chem 53:5707–5715

    Article  CAS  PubMed  Google Scholar 

  28. Heikamp K, Bajorath J (2011) Large-scale similarity search profiling of CHEMBL compound data sets. J Chem Inf Model 51:1831–1839

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Jürgen Bajorath .

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Bajorath, J. (2017). Molecular Similarity Concepts for Informatics Applications. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_13

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

  • eBook Packages: Springer Protocols

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