Using Hybrid Similarity-Based Collaborative Filtering Method for Compound Activity Prediction

  • Jun Ma
  • Ruisheng Zhang
  • Yongna Yuan
  • Zhili Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


It is important for researchers to predict compound activity to the targets quickly and effectively in the field of drug design. In the paper, the problem of compound activity prediction is converted to the recommendations in the field of e-commerce, compounds are viewed as users, and protein targets are viewed as items. A rating matrix is extracted by IC50 of each compound to targets, there are four filtering recommendation algorithms could be used for predicting compound activity. In order to improve the accuracy of prediction, the hybrid similarity-based Collaborative Filtering (HybridSimCF) Method is proposed, the method will combine the similarity of the compound structure and the similarity based on the rating matrix to predict the activity. Through compared with other three collaborative filtering methods, HybridSimCF has better results. It not only improves the values of RMSE and MAE, but also effectively solves the cold start problem. The method can quickly and effectively solve the prediction of compound activity.


Drug design QSAR Compound activity Rating prediction Machine learning Collaborative filtering 



This work was supported by the Fundamental Research Funds for the National Natural Science Foundation of China (Grant No. 21503101, No. 61702240), the Natural Science Foundation of Gansu Province, China (Grant No. 1506RJZA223), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (Grant No. External department of Education [2015] 311) and the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2017-191).


  1. 1.
    Darnag, R., Mazouz, E.L.M., Schmitzer, A., Villemin, D., Jarid, A., Cherqaoui, D.: Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives. Eur. J. Med. Chem. 45, 1590 (2010)CrossRefGoogle Scholar
  2. 2.
    Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P.A., Igglessi-Markopoulou, O., Kollias, G.: A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs. Mol. Divers. 14, 225–235 (2010)CrossRefGoogle Scholar
  3. 3.
    Mehmood, T., Liland, K.H., Snipen, L., Sæbø, S.: A review of variable selection methods in Partial Least Squares Regression. Chemom. Intell. Lab. Syst. 118, 62–69 (2012)CrossRefGoogle Scholar
  4. 4.
    Sharma, M.C., Sharma, S., Sahu, N.K., Kohli, D.V.: QSAR studies of some substituted imidazolinones angiotensin II receptor antagonists using Partial Least Squares Regression (PLSR) method based feature selection. J. Saudi Chem. Soc. 17, 219–225 (2013)CrossRefGoogle Scholar
  5. 5.
    Dahl, G.E., Jaitly, N., Salakhutdinov, R.: Multi-task Neural Networks for QSAR Predictions. Computer Science (2014)Google Scholar
  6. 6.
    Myint, K.Z., Wang, L., Tong, Q., Xie, X.Q.: Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol. Pharm. 9, 2912–2923 (2012)CrossRefGoogle Scholar
  7. 7.
    Dearden, J.C., Rowe, P.H.: Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation. In: Cartwright, H. (ed.) Artificial Neural Networks. MMB, vol. 1260, pp. 65–88. Springer, New York (2015). Scholar
  8. 8.
    Gupta, S., Basant, N., Singh, K.P.: Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose. Ecotoxicology 24, 873–886 (2015)CrossRefGoogle Scholar
  9. 9.
    Burton, J., Danloy, E., Vercauteren, D.P.: Fragment-based prediction of cytochromes P450 2D6 and 1A2 inhibition by recursive partitioning. SAR QSAR Environ. Res. 20, 185–205 (2009)CrossRefGoogle Scholar
  10. 10.
    Choi, S.Y., Shin, J.H., Ryu, C.K., Nam, K.Y., No, K.T., Choo, H.Y.P.: The development of 3D-QSAR study and recursive partitioning of heterocyclic quinone derivatives with antifungal activity. Bioorgan. Med. Chem. 14, 1608–1617 (2006)CrossRefGoogle Scholar
  11. 11.
    Chandrasekaran, M., Sakkiah, S., Lee, K.W.: Combined chemical feature-based assessment and Bayesian model studies to identify potential inhibitors for Factor Xa. Med. Chem. Res. 21, 4083–4099 (2012)CrossRefGoogle Scholar
  12. 12.
    Yang, Y., Zhang, W., Cheng, J., Tang, Y., Peng, Y., Li, Z.: Pharmacophore, 3D-QSAR, and Bayesian model analysis for ligands binding at the benzodiazepine site of GABAA receptors: the key roles of amino group and hydrophobic sites. Chem. Biol. Drug Des. 81, 583–590 (2013)CrossRefGoogle Scholar
  13. 13.
    Kim, J.H., Chong, H.C., Kang, S.M., Lee, J.Y., Lee, G.N., Hwang, S.H., Kang, N.S.: The predictive QSAR model for hERG inhibitors using Bayesian and random forest classification method. Bull. Korean Chem. Soc. 32, 1237–1240 (2011)CrossRefGoogle Scholar
  14. 14.
    Singh, H., Singh, S., Singla, D., Agarwal, S.M., Raghava, G.P.S.: QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol. Direct 10, 10 (2015)CrossRefGoogle Scholar
  15. 15.
    Fechner, N., Hinselmann, G., Jahn, A., Zell, A.: Kernel-based estimation of the applicability domain of QSAR models. J. Cheminform. 2, 1 (2010)CrossRefGoogle Scholar
  16. 16.
    Tebby, C., Mombelli, E.: A kernel-based method for assessing uncertainty on individual QSAR predictions. QSAR Comb. Sci. 31, 741–751 (2015)Google Scholar
  17. 17.
    Erhan, D., L’Heureux, P.J., Shi, Y.Y., Bengio, Y.: Collaborative filtering on a family of biological targets. J. Chem. Inf. Model. 46, 626 (2006)CrossRefGoogle Scholar
  18. 18.
    Erhan, D.: Collaborative filtering techniques for drug discovery (2006)Google Scholar
  19. 19.
    Ning, X., Rangwala, H., Karypis, G.: Multi-assay-based structure-activity relationship models: improving structure-activity relationship models by incorporating activity information from related targets. J. Chem. Inf. Model. 49, 2444 (2009)CrossRefGoogle Scholar
  20. 20.
    Zhang, R., Li, J., Lu, J., Hu, R., Yuan, Y., Zhao, Z.: Using deep learning for compound selectivity prediction. Curr. Comput. Aided Drug Des. 12, 1 (2016)CrossRefGoogle Scholar
  21. 21.
    Rosenbaum, L., Dörr, A., Bauer, M.R., Boeckler, F.M., Zell, A.: Inferring multi-target QSAR models with taxonomy-based multi-task learning. J. Cheminform. 5, 33 (2013)CrossRefGoogle Scholar
  22. 22.
    Gaulton, A., Bellis, L.J., Bento, A.P., Chambers, J., Davies, M., Hersey, A., Light, Y., Mcglinchey, S., Michalovich, D., Allazikani, B.: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, 1100 (2012)CrossRefGoogle Scholar
  23. 23.
    Cheng, Y.C., Prusoff, W.H.: Relation between the inhibition constant (K1) and the concentration of inhibitor which causes fifty percent inhibition (I50) of an enzymic reaction. Biochem. Pharmacol. 22, 3099–30108 (1973)CrossRefGoogle Scholar
  24. 24.
    Xia, N.: Machine learning and data mining methods for recommender systems and chemical informatics. University of Minnesota (2012)Google Scholar
  25. 25.
    DRAGON Homepage:
  26. 26.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, vol. 158 (1999)Google Scholar
  27. 27.
    Shi, J., Chen, J., Bao, Z.: An application study on collaborative filtering in e-commerce. In: International Conference on Service Systems and Service Management, vol. 1 (2011)Google Scholar
  28. 28.
    Stigler, S.M.: Francis Galton’s account of the invention of correlation. Stat. Sci. 4, 73 (1989)MathSciNetCrossRefGoogle Scholar
  29. 29.
    You, H., Li, H., Wang, Y., Zhao, Q.: An improved collaborative filtering recommendation algorithm combining item clustering and slope one scheme. Lect. Notes Eng. Comput. Sci. 2215, 313–316 (2015)Google Scholar
  30. 30.
    Sedhain S., Braziunas D., Braziunas D., Christensen J., Christensen J.: Social collaborative filtering for cold-start recommendations. In: ACM Conference on Recommender Systems, vol. 345 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Ma
    • 1
  • Ruisheng Zhang
    • 1
  • Yongna Yuan
    • 1
  • Zhili Zhao
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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