Predicting Drug Target Interaction by Integrating Drug Fingerprint and Drug Side Effect Using Machine Learning

  • Abdelrahman Saad
  • Fahima A. MaghrabyEmail author
  • Yasser M. OmarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Drug discovery is an important step before drug development. Drug discovery is the process of identifying, testing a drug before medical use. Drugs are used to cure diseases by interacting with the target, which is the protein in the human cells. Many resources are wasted (cost and time) on lab experiments to discover drugs and its application. Yet machine learning enhanced the process of drug discovery and the prediction of drug-target interaction, which helped in predicting new drugs and finding more applications for old drugs. Predicting drug-target interaction starting by studying the nature of drugs and its properties. Most of the datasets existing are drugs, targets and their interactions datasets. We compiled our dataset to include side effect as drug feature. The dataset contains 400 drugs, 794 targets and 3990 side effects. In this study, a machine-learning model is implemented using three different classifiers: Decision Tree, Random Forest (RF) and K-Nearest Neighbors (K-NN) for classification. Drug fingerprint and side effect were used as input features to train our model. Three different experiments were conducted using fingerprint, side effect and both fingerprint and side effect. Results showed improvement in prediction when integrating both drug fingerprint and side effect. K-NN scored best results in the three experiment with an average accuracy of 94.69%.


Drug DTI Fingerprint Side effect Target 


  1. 1.
    Shi, J.-Y., Yiu, S.-M., Li, Y., Leung, H.C.M., Chin, F.Y.L.: Predicting drug–target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods 83, 98–104 (2015)CrossRefGoogle Scholar
  2. 2.
    Lan, C., Chandrasekarany, S., Huan, J.: A distributed and privatized framework for drug-target interaction prediction. In: International Conference on Bioinformatics and Biomedicine (BIBM), pp. 731–734. IEEE (2016)Google Scholar
  3. 3.
    Statistics: DrugBank. Accessed Nov 2018
  4. 4.
    Bolton, E., Wang, Y., Thiessen, P., Bryant, S.: PubChem: integrated platform of small molecules and biological activities. Ann. Rep. Comput. Chem. 4, 217–241 (2008)CrossRefGoogle Scholar
  5. 5.
    Hurle, M., Yang, L., Xie, Q., Rajpal, D., Sanseau, P., Agarwal, P.: Computational drug repositioning: from data to therapeutics. Clin. Pharmacol. Ther. 93(4), 335–341 (2013)CrossRefGoogle Scholar
  6. 6.
    Chen, X., Yan, C., Zhang, X., Zhang, X., Dai, F., Yin, J., Zhang, Y.: Drug–target interaction prediction: databases, web servers and computational models. Briefings Bioinf. 17(4), 696–712 (2015)CrossRefGoogle Scholar
  7. 7.
    Li, H., Gao, Z., Kang, L., Zhang, H., Yang, K., Yu, K., Luo, X., Zhu, W., Chen, K., Shen, J., Wang, X., Jiang, H.: TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res. 34(Web Server), W219–W224 (2006)CrossRefGoogle Scholar
  8. 8.
    Kanehisa, M.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34(90001), D354–D357 (2006)CrossRefGoogle Scholar
  9. 9.
    Schomburg, I.: BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32(90001), 431D–433D (2004)CrossRefGoogle Scholar
  10. 10.
    Kuhn, M., Szklarczyk, D., Franceschini, A., Mering, C., Jensen, L., Bork, P.: STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 40(D1), D876–D880 (2011)CrossRefGoogle Scholar
  11. 11.
    Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(suppl_1), D901–D906 (2007)CrossRefGoogle Scholar
  12. 12.
    Coelho, E., Oliveira, J., Arrais, J.: Ensemble-based methodology for the prediction of drug-target interactions. In: 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 36–41. IEEE (2016)Google Scholar
  13. 13.
    Wishart, D.S.: DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34(90001), D668–D672 (2006)CrossRefGoogle Scholar
  14. 14.
    Yamanishi, Y., Kotera, M., Kanehisa, M., Goto, S.: Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26(12), i246–i254 (2010)CrossRefGoogle Scholar
  15. 15.
    Galeano, D., Paccanaro, A.: Drug targets prediction using chemical similarity. In: XLII Latin American Computing Conference (CLEI), pp. 1–7. IEEE (2016)Google Scholar
  16. 16.
    Stark, C.: BioGRID: a general repository for interaction datasets. Nucleic acids Res. 34(suppl 1), D535–D539 (2006)CrossRefGoogle Scholar
  17. 17.
    Hao, M., Bryant, S., Wang, Y.: Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci. Rep. 7(1), 40376 (2017)CrossRefGoogle Scholar
  18. 18.
    Sinha, A., Singh, P., Prakash, A., Pal, D., Dube, A., Kumar, A.: Putative drug and vaccine target identification in leishmania donovani membrane proteins using naïve bayes probabilistic classifier. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 204–211 (2017)CrossRefGoogle Scholar
  19. 19.
    Kumar, A., Misra, P., Sisodia, B., Shasany, A., Sundar, S., Dube, A.: Proteomic analyses of membrane enriched proteins of Leishmania donovani Indian clinical isolate by mass spectrometry. Parasitol. Int. 64(4), 36–42 (2015)CrossRefGoogle Scholar
  20. 20.
    Li, Z., Han, P., You, Z.-H., Li, X., Zhang, Y., Yu, H., Nie, R., Chen, X.: In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci. Rep. 7(1) (2017)Google Scholar
  21. 21.
    Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E.G., Gewiess, A., Jensen, L.J., Schneider, R., Skoblo, R., Russell, R.B., Bourne, P.E., Bork, P., Preissner, R.: SuperTarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36(Database), D919–D922 (2007)CrossRefGoogle Scholar
  22. 22.
    Azuaje, F., Zhang, L., Devaux, Y., Wagner, D.: Drug-target network in myocardial infarction reveals multiple side effects of unrelated drugs. Sci. Rep. 1(1), 52 (2011)CrossRefGoogle Scholar
  23. 23.
    Cao, D.-S., Liu, S., Xu, Q.-S., Lu, H.-M., Huang, J.-H., Hu, Q.-N., Liang, Y.-Z.: Large-scale prediction of drug–target interactions using protein sequences and drug topological structures. Anal. Chim. Acta 752, 1–10 (2012)CrossRefGoogle Scholar
  24. 24.
    Cao, D.-S., Hu, Q.-N., Xu, Q.-S., Yang, Y.-N., Zhao, J.-C., Lu, H.-M., Zhang, L.-X., Liang, Y.-Z.: In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint. Anal. Chim. Acta 692(1–2), 50–56 (2011)CrossRefGoogle Scholar
  25. 25.
    Campillos, M., Kuhn, M., Gavin, A., Jensen, L., Bork, P.: Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008)CrossRefGoogle Scholar
  26. 26.
    Fayz, S., Rizka, M., Maghraby, F.: Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques. IEEE Access 1 (2018)Google Scholar
  27. 27.
    Wu, Y., Wang, H., Wu, F.: Automatic classification of pulmonary tuberculosis and sarcoidosis based on random forest. In: 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE (2017)Google Scholar
  28. 28.
    Bombara, G., Vasile, C.-I., Penedo, F., Yasuoka, H., Beltaz, C.: A decision tree approach to data classification using signal temporal logic. In: Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control - HSCC 2016 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Arab Academy for Science, Technology and Maritime Transport (AASTMT)CairoEgypt

Personalised recommendations