Journal of Computer-Aided Molecular Design

, Volume 30, Issue 5, pp 413–424 | Cite as

TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models

  • Zhi-Jiang Yao
  • Jie Dong
  • Yu-Jing Che
  • Min-Feng Zhu
  • Ming Wen
  • Ning-Ning Wang
  • Shan Wang
  • Ai-Ping Lu
  • Dong-Sheng Cao
Article

Abstract

Drug–target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug–drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user’s molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75–100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug–drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Keywords

Web server SAR models Drug–target interaction Multi-target SAR Naïve Bayes 

Notes

Acknowledgments

We would like to thank the Django group for their great Django server. We would also like to thank Dr. Peter Ertl for his JME molecular editor, and we thank the developers of D3.js. We would also like to thank three anonymous referees and the editor for their constructive comments, which greatly helped improve upon the original version of the manuscript.

Funding

This work has been financially supported by grants from the Project of Innovation-driven Plan in Central South University, the National Natural Science Foundation of China (Grants No. 81402853), the National key basic research program (Grants No. 2015CB910700), and the Postdoctoral Science Foundation of Central South University, the Chinese Postdoctoral Science Foundation (2014T70794, 2014M562142). The studies meet with the approval of the university’s review board.

Compliance with ethical standards

Conflict of interest

None.

Supplementary material

10822_2016_9915_MOESM1_ESM.xls (101 kb)
Supplementary material 1 (XLS 101 kb)
10822_2016_9915_MOESM2_ESM.xls (199 kb)
Supplementary material 2 (XLS 199 kb)
10822_2016_9915_MOESM3_ESM.xls (1.8 mb)
Supplementary material 3 (XLS 1844 kb)
10822_2016_9915_MOESM4_ESM.xls (534 kb)
Supplementary material 4 (XLS 534 kb)

References

  1. 1.
    Yıldırım MA, Goh K-I, Cusick ME, Barabási A-L, Vidal M (2007) Nat Biotechnol 25(10):1119CrossRefGoogle Scholar
  2. 2.
    Nunez S, Venhorst J, Kruse CG (2011) Drug Discov Today 17(1):10Google Scholar
  3. 3.
    Gottlieb A, Stein GY, Ruppin E, Sharan R (2011) Mol Syst Biol 7(1):496CrossRefGoogle Scholar
  4. 4.
    Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L (2011) Nucleic Acids Res 39(Suppl 2):W492Google Scholar
  5. 5.
    Cao DS, Xiao N, Li YJ, Zeng WB, Liang YZ, Lu AP, Xu QS, Chen A (2015) CPT: pharmacometrics & systems. Pharmacology 4(9):498Google Scholar
  6. 6.
    Wienkers LC, Heath TG (2005) Nat Rev Drug Discov 4(10):825CrossRefGoogle Scholar
  7. 7.
    Luo H, Zhang P, Huang H, Huang J, Kao E, Shi L, He L, Yang L (2014) Nucleic Acids Res 42(W1):W46Google Scholar
  8. 8.
    Tatonetti NP, Ye PP, Daneshjou R, Altman RB (2012) Sci Transl Med 4(125):125ra31CrossRefGoogle Scholar
  9. 9.
    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A (2010) Proc Natl Acad Sci 107(33):14621CrossRefGoogle Scholar
  10. 10.
    Iorio F, Tagliaferri R, Bernardo Dd (2009) J Comput Biol 16(2):241CrossRefGoogle Scholar
  11. 11.
    Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J (2006) Nucleic Acids Res 34(suppl 2):W219CrossRefGoogle Scholar
  12. 12.
    Kharkar PS, Warrier S, Gaud RS (2014) Fut Med Chem 6(3):333CrossRefGoogle Scholar
  13. 13.
    Lee M, Kim D (2012) BMC Bioinformatics 13(Suppl 17):S6Google Scholar
  14. 14.
    Cao D-S, Liang Y-Z, Deng Z, Hu Q-N, He M, Xu Q-S, Zhou G-H, Zhang L-X, Deng Z, Liu S (2013) PLoS One 8(4):e57680CrossRefGoogle Scholar
  15. 15.
    Cao D-S, Liu S, Xu Q-S, Lu H-M, Huang J-H, Hu Q-N, Liang Y-Z (2012) Anal Chim Acta 752:1CrossRefGoogle Scholar
  16. 16.
    Bredel M, Jacoby E (2004) Nat Rev Genet 5(4):262CrossRefGoogle Scholar
  17. 17.
    Klabunde T (2007) Br J Pharmacol 152(1):5CrossRefGoogle Scholar
  18. 18.
    Nagamine N, Sakakibara Y (2007) Bioinformatics 23(15):2004CrossRefGoogle Scholar
  19. 19.
    He Z, Zhang J, Shi X-H, Hu L-L, Kong X, Cai Y-D, Chou K-C (2010) PLoS One 5(3):e9603CrossRefGoogle Scholar
  20. 20.
    Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, Li X, Zhou W, Wang W, Wang Y (2012) PLoS One 7(5):e37608CrossRefGoogle Scholar
  21. 21.
    Xiao X, Min J-L, Wang P, Chou K-C (2013) PLoS One 8(8):e72234CrossRefGoogle Scholar
  22. 22.
    Cheng F, Zhou Y, Li J, Li W, Liu G, Tang Y (2012) Mol BioSyst 8(9):2373CrossRefGoogle Scholar
  23. 23.
    Cheng F, Zhou Y, Li W, Liu G, Tang Y (2012) PLoS One 7(7):e41064CrossRefGoogle Scholar
  24. 24.
    Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y (2012) PLoS Comput Biol 8(5):e1002503CrossRefGoogle Scholar
  25. 25.
    Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Bioinformatics 24(13):i232CrossRefGoogle Scholar
  26. 26.
    Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Science 321(5886):263CrossRefGoogle Scholar
  27. 27.
    Bleakley K, Yamanishi Y (2009) Bioinformatics 25(18):2397CrossRefGoogle Scholar
  28. 28.
    Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, Whaley R, Glennon RA, Hert J, Thomas KLH, Edwards DD, Shoichet BK, Roth BL (2009) Nature 462(7270):175CrossRefGoogle Scholar
  29. 29.
    Xia Z, Wu L-Y, Zhou X, Wong S (2010) BMC Syst Biol 4(Suppl 2):S6CrossRefGoogle Scholar
  30. 30.
    Jacob L, Vert J-P (2008) Bioinformatics 24(19):2149CrossRefGoogle Scholar
  31. 31.
    van Laarhoven T, Nabuurs SB, Marchiori E (2011) Bioinformatics 27(21):3036CrossRefGoogle Scholar
  32. 32.
    Chen X, Liu M-X, Yan G-Y (2012) Mol BioSyst 8(7):1970CrossRefGoogle Scholar
  33. 33.
    Mei J-P, Kwoh C-K, Yang P, Li X-L, Zheng J (2013) Bioinformatics 29(2):238CrossRefGoogle Scholar
  34. 34.
    Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y (2012) Bioinformatics 28(18):i522CrossRefGoogle Scholar
  35. 35.
    Csermely P, Agoston V, Pongor S (2005) Trends Pharmacol Sci 26(4):178CrossRefGoogle Scholar
  36. 36.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Nucleic Acids Res 35(suppl 1):D198CrossRefGoogle Scholar
  37. 37.
    Scott DE, Coyne AG, Hudson SA, Abell C (2012) Biochemistry 51(25):4990CrossRefGoogle Scholar
  38. 38.
    Cao DS, Yang YN, Zhao JC, Yan J, Liu S, Hu QN, Xu QS, Liang YZ (2012) J Chemom 26(1–2):7CrossRefGoogle Scholar
  39. 39.
    Cao D-S, Xu Q-S, Hu Q-N, Liang Y-Z (2013) Bioinformatics 29(8):1092CrossRefGoogle Scholar
  40. 40.
    Cao D-S, Liang Y-Z, Yan J, Tan G-S, Xu Q-S, Liu S (2013) J Chem Inf Model 53(11):3086CrossRefGoogle Scholar
  41. 41.
    Bender A, Mussa HY, Glen RC, Reiling S (2004) J Chem Inf Comput Sci 44(1):170CrossRefGoogle Scholar
  42. 42.
    Wang S, Li Y, Wang J, Chen L, Zhang L, Yu H, Hou T (2012) Mol Pharm 9(4):996CrossRefGoogle Scholar
  43. 43.
    Watson P (2008) J Chem Inf Model 48(1):166CrossRefGoogle Scholar
  44. 44.
    Zhang L, Zhang Y, Zhao P, Huang S-M (2009) AAPS J 11(2):300CrossRefGoogle Scholar
  45. 45.
    Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen X-w, Matheny ME, Xu H (2012) J Am Med Inf Assoc 19(E1):E28CrossRefGoogle Scholar
  46. 46.
    Park Y, Marcotte EM (2012) Nat Methods 9(12):1134CrossRefGoogle Scholar
  47. 47.
    Pahikkala T, Airola A, Pietilä S, Shakyawar S, Szwajda A, Tang J, Aittokallio T (2015) Brief Bioinform 16(2):325Google Scholar
  48. 48.
    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN (2006) Science 313(5795):1929CrossRefGoogle Scholar
  49. 49.
    Yamanishi Y, Kotera M, Moriya Y, Sawada R, Kanehisa M, Goto S (2014) Nucleic Acids Res 42(W1):W39CrossRefGoogle Scholar
  50. 50.
    Li G-H, Huang J-F (2012) Bioinformatics 28(24):3334CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhi-Jiang Yao
    • 1
    • 2
  • Jie Dong
    • 1
  • Yu-Jing Che
    • 3
  • Min-Feng Zhu
    • 3
  • Ming Wen
    • 2
  • Ning-Ning Wang
    • 1
  • Shan Wang
    • 2
  • Ai-Ping Lu
    • 4
  • Dong-Sheng Cao
    • 1
    • 4
  1. 1.School of Pharmaceutical SciencesCentral South UniversityChangshaPeople’s Republic of China
  2. 2.College of Chemistry and Chemical EngineeringCentral South UniversityChangshaPeople’s Republic of China
  3. 3.School of Mathematics and StatisticsCentral South UniversityChangshaPeople’s Republic of China
  4. 4.Institute of Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese MedicineHong Kong Baptist UniversityHong KongPeople’s Republic of China

Personalised recommendations