Orphan Drug Legislation with Data Fusion Rules Using Multiple Fingerprints Measurements

  • Moustafa Zein
  • Ahmed Abdo
  • Ammar Adl
  • Aboul Ella Hassanien
  • Mohamed F. Tolba
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


The orphan drug certification process from the European committee is depending on experts opinions that it is not similar to any other drug, this stage is very complicated and those opinions differ based on the expertise. So, this paper introduces computational model that gives one accurate probability of similarity, using multiple fingerprints measurements to similarity, and fuse these measurements by data fusion rules, that give one probability of similarity helping experts to determine that drug is similar to existing anyone or not.


Similarity coefficients fingerprints Orphan drug drug legislation Data fusion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cheminformatics and machine learning software (2013), (accessed: 2013)
  2. 2.
  3. 3.
    Drug bank (2013),
  4. 4.
    Bender, A.: How similar are those molecules after all, use two descriptors and you will have three different answers. Expert Opinion on Drug Discovery 5(12), 1141–1151 (2010)CrossRefGoogle Scholar
  5. 5.
    Bender, A., Glen, R.C.: Molecular similarity: a key technique in molecular informatics. Organic & Biomolecular Chemistry 2(22), 3204–3218 (2004)CrossRefGoogle Scholar
  6. 6.
    Bender, A., Jenkins, J.L., Scheiber, J., Sukuru, S.C.K., Glick, M., Davies, J.W.: How similar are similarity searching methods? a principal component analysis of molecular descriptor space. Journal of Chemical Information and Modeling 49(1), 108–119 (2009)CrossRefGoogle Scholar
  7. 7.
    Bonnet, P.: Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? a comparative assessment between medicinal and computational chemists. European Journal of Medicinal Chemistry 54, 679–689 (2012)CrossRefGoogle Scholar
  8. 8.
    Cross, S., Baroni, M., Carosati, E., Benedetti, P., Clementi, S.: Flap: Grid molecular interaction fields in virtual screening. validation using the dud data set. Journal of Chemical Information and Modeling 50(8), 1442–1450 (2010)CrossRefGoogle Scholar
  9. 9.
    Dutt, R., Madan, A.: Predicting biological activity: Computational approach using novel distance based molecular descriptors. Computers in Biology and Medicine 42(10), 1026–1041 (2012)CrossRefGoogle Scholar
  10. 10.
    Franco, P., Porta, N., Holliday, J.D., Willett, P.: The use of 2dngerprint methods to support the assessment of structural similarity in orphan drug legislation. Journal of Cheminformatics 6(1), 5 (2014)CrossRefGoogle Scholar
  11. 11.
    Gedeck, P., Rohde, B., Bartels, C.: Qsar-how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. Journal of Chemical Information and Modeling 46(5), 1924–1936 (2006)CrossRefGoogle Scholar
  12. 12.
    Geppert, H., Vogt, M., Bajorath, J.: Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. Journal of Chemical Information and Modeling 50(2), 205–216 (2010)CrossRefGoogle Scholar
  13. 13.
    Hert, J., Willett, P., Wilton, D.J., Acklin, P., Azzaoui, K., Jacoby, E., Schuenhauer, A.: Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Organic & Biomolecular Chemistry 2(22), 3256–3266 (2004)CrossRefGoogle Scholar
  14. 14.
    Jain, A.N., Nicholls, A.: Recommendations for evaluation of computational methods. Journal of Computer-aided Molecular Design 22(3-4), 133–139 (2008)CrossRefGoogle Scholar
  15. 15.
    Manley, P.W., Stie, N., Cowan-Jacob, S.W., Kaufman, S., Mestan, J., Wartmann, M., Wiesmann, M., Woodman, R., Gallagher, N.: Structural resemblances and comparisons of the relative pharmacological properties of imatinib and nilotinib. Bioorganic Medicinal Chemistry 18(19), 6977–6986 (2010)CrossRefGoogle Scholar
  16. 16.
    McGaughey, G.B., Sheridan, R.P., Bayly, C.I., Culberson, J.C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.F., Cornell, W.D.: Comparison of topological, shape, and docking methods in virtual screening. Journal of Chemical Information and Modeling 47(4), 1504–1519 (2007)CrossRefGoogle Scholar
  17. 17.
    Melnikova, I.: Rare diseases and orphan drugs. Nature Reviews Drug Discovery 11(4), 267–268 (2012)CrossRefGoogle Scholar
  18. 18.
    Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., Greyson, D.: The cost of drug development: a systematic review. Health Policy 100(1), 4–17 (2011)CrossRefGoogle Scholar
  19. 19.
    Riniker, S., Landrum, G.A.: Open-source platform to benchmark fingerprints for ligand-based virtual screening. Journal of Cheminformatics 5, 26 (2013)CrossRefGoogle Scholar
  20. 20.
    Ripphausen, P., Nisius, B., Bajorath, J.: State-of-the-art in ligand-based virtual screening. Drug Discovery Today 16(9), 372–376 (2011)CrossRefGoogle Scholar
  21. 21.
    Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of Chemical Information and Modeling 50(5), 742–754 (2010)CrossRefGoogle Scholar
  22. 22.
    Stumpfe, D., Bajorath, J.: Similarity searching. Wiley Interdisciplinary Reviews: Computational Molecular Science 1(2), 260–282 (2011)Google Scholar
  23. 23.
    Swann, S.L., Brown, S.P., Muchmore, S.W., Patel, H., Merta, P., Locklear, J., Hajduk, P.J.: A unified, probabilistic framework for structure-and ligand-based virtual screening. Journal of Medicinal Chemistry 54(5), 1223–1232 (2011)CrossRefGoogle Scholar
  24. 24.
    Todeschini, R., Consonni, V., Xiang, H., Holliday, J., Buscema, M., Willett, P.: Similarity coeficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. Journal of Chemical Information and Modeling 52(11), 2884–2901 (2012); Orphan drug legislation with data fusion rules 11Google Scholar
  25. 25.
    Truchon, J.F., Bayly, C.I.: Evaluating virtual screening methods: good and bad metrics for the early recognition problem. Journal of Chemical Information and Modeling 47(2), 488–508 (2007)CrossRefGoogle Scholar
  26. 26.
    Willett, P.: Similarity methods in chemoinformatics. Annual Review of Information Science and Technology 43(1), 1–117 (2009)CrossRefGoogle Scholar
  27. 27.
    Willett, P.: Combination of similarity rankings using data fusion. Journal of Chemical Information and Modeling 53(1), 1–10 (2013)CrossRefGoogle Scholar
  28. 28.
    Bender, A., Jenkins, J.L., Scheiber, J., Sukuru, S.C.K., Glick, M., Davies, J.W.: How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J. Chem. Inf. Model. 49, 108–119 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Moustafa Zein
    • 1
  • Ahmed Abdo
    • 1
  • Ammar Adl
    • 1
  • Aboul Ella Hassanien
    • 1
    • 2
  • Mohamed F. Tolba
    • 3
  • Václav Snášel
    • 4
  1. 1.Scientific Research Group in Egypt (SRGE)CairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt
  3. 3.Faculty of MedicineAin Shams UniversityCairoEgypt
  4. 4.Electrical Engineering & Computer ScienceVSB-TU of OstravaOstravaCzech Republic

Personalised recommendations