Data Integration with Self-organising Neural Network Reveals Chemical Structure and Therapeutic Effects of Drug ATC Codes

  • Ken McGarryEmail author
  • Ennock Assamoha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)


Anatomical Therapeutic Codes (ATC) are a drug classification system which is extensively used in the field of drug development research. There are many drugs and medical compounds that as yet do not have ATC codes, it would be useful to have codes automatically assigned to them by computational methods. Our initial work involved building feedforward multi-layer perceptron models (MLP) but the classification accuracy was poor. To gain insights into the problem we used the Kohonen self-organizing neural network to visualize the relationship between the class labels and the independent variables. The information gained from the learned internal clusters gave a deeper insight into the mapping process. The ability to accurately predict ATC codes was unbalanced due to over and under representation of some ATC classes. Further difficulties arise because many drugs have several, quite different ATC codes because they have many therapeutic uses. We used chemical fingerprint data representing a drugs chemical structure and chemical activity variables. Evaluation metrics were computed, analysing the predictive performance of various self-organizing models.


Kohonen Prediction ATC codes Chemical fingerprints 


  1. 1.
    Chen, F., Jiang, Z.: Prediction of drug’s anatomical therapeutic chemical (ATC) code by integrating drug-domain data. J. Biomed. Inf. 58, 80–88 (2015)CrossRefGoogle Scholar
  2. 2.
    Cheng, X., Zhao, S., Xiao, X., Chou, K.: iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic codes. Bioinformatics 33(3), 341–346 (2016)Google Scholar
  3. 3.
    Dunkel, M., Gunther, S., Ahmed, J., Wittig, B.: Superpred: drug classification and target prediction. Nucleic Acids Res. 36, W55–W59 (2008)CrossRefGoogle Scholar
  4. 4.
    Gurulingappa, H., Kolarik, C., Hofmann-Apitius, M., Fluck, J.: Concept-based semi-automatic classification of drugs. J. Chem. Inf. Model. 49(8), 1986–1992 (2009)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proc. IEEE 84(10), 1358–1383 (1996)CrossRefGoogle Scholar
  6. 6.
    Law, V., Knox, C., et al.: Drugbank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091–D1097 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, Z., Guo, F., Gu, J., Wang, Y., Li, Y., Wang, D., Li, D., He, F.: Similarity-based prediction for anatomical therapeutic chemical classification of drugs by integrating multiple data sources. Bioinformatics 31(11), 1788–1795 (2015)CrossRefGoogle Scholar
  8. 8.
    Malone, J., McGarry, K., Bowerman, C., Wermter, S.: Rule extraction from kohonen neural networks. Neural Comput. Appl. J. 15(1), 9–17 (2006)CrossRefGoogle Scholar
  9. 9.
    McGarry, K., Daniel, U.: Data mining open source databases for drug repositioning using graph based techniques. Drug Discov. World 16(1), 64–71 (2015)Google Scholar
  10. 10.
    McGarry, K., Slater, N., Amaning, A.: Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects. In: The 15th UK Workshop on Computational Intelligence, UKCI-2015. University of Exeter, UK (7th-9th September 2015)Google Scholar
  11. 11.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2015).
  12. 12.
    Ultsch, A., Korus, D.: Automatic acquisition of symbolic knowledge from subsymbolic neural nets. In: Proceedings of the 3rd European Conference on Intelligent Techniques and Soft Computing, pp. 326–331 (1995)Google Scholar
  13. 13.
    Ultsch, A., Mantyk, R., Halmans, G.: Connectionist knowledge acquisition tool: CONKAT. In: Hand, J. (ed.) Artificial Intelligence Frontiers in Statistics: AI and statistics III, pp. 256–263. Chapman and Hall, London (1993)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Chen, S., Deng, N., Wang, Y.: Network predicting drug’s anatomical therapeutic chemical code. Bioinformatics 29(10), 1317–1324 (2013)CrossRefGoogle Scholar
  15. 15.
    Wehrens, R., Buydens, L.: Self and super-organising maps in r: the Kohonen package. J. Stat. Softw. 21(5) (2007).
  16. 16.
    Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Model. 28(1), 316 (1988)CrossRefGoogle Scholar
  17. 17.
    Wu, L., Liu, N., Wang, Y., Fan, X.: Relating anatomical therapeutic indications by the ensemble similarity of drug sets. J. Chem. Inf. Model. 53, 2154–2160 (2013)CrossRefGoogle Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Pharmacy and Pharmaceutical Sciences, Faculty of Health Sciences and WellbeingUniversity of SunderlandSunderlandUK

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