Skip to main content
SpringerLink
Log in

Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into successful clinical candidates remains a big challenge. In recent years, the availability of massive structural and biological data of diverse small molecules and macromolecules has helped the researchers to deep mine the multidimensional data with the help of artificial intelligence-based predictive tools to draw useful insights on the structural features of biological or therapeutic significance. The aim of this study was to utilize the available data on small molecule (SH2)-containing protein tyrosine phosphatase 2 (SHP2) inhibitors to build and develop machine learning (ML) models that can predict the SHP2 inhibitory potential of new compounds. The dataset contained 2739 unique small molecule SHP2 inhibitors obtained from the BindingDB, ChEMBL and recent literature. After curation of the data, the predictive models such as XGBoost, K nearest neighbours, neural networks were developed and validated through a tenfold cross-validation testing procedure. Out of the seven models developed, the XGBoost model showed an excellent performance with ROC AUC score of 0.96 and accuracy of 0.97 on the test data. Moreover, the Shapley Additive Explanations method was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. In summary, the XGBoost model developed in this study can be useful in the identification of novel SHP2 inhibitors and therefore, can accelerate the discovery of novel therapeutics for cancer therapy.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Feng GS, Shen R, Heng HH et al (1994) Receptor-binding, tyrosine phosphorylation and chromosome localization of the mouse SH2-containing phosphotyrosine phosphatase Syp. Oncogene 9:1545–1550

    CAS  PubMed  Google Scholar 

  2. Feng GS, Hui CC, Pawson T (1993) Sh2-containing phosphotyrosine phosphatase as a target of protein-tyrosine kinases. Science 259:1607–1611. https://doi.org/10.1126/SCIENCE.8096088

    Article  CAS  PubMed  Google Scholar 

  3. Feng G-S, Pawson T (1994) Phosphotyrosine phosphatases with SH2 domains: regulators of signal transduction. Trends Genet 10:54–58. https://doi.org/10.1016/0168-9525(94)90149-X

    Article  CAS  PubMed  Google Scholar 

  4. Hof P, Pluskey S, Dhe-Paganon S et al (1998) Crystal structure of the tyrosine phosphatase SHP-2. Cell 92:441–450. https://doi.org/10.1016/S0092-8674(00)80938-1

    Article  CAS  PubMed  Google Scholar 

  5. Bennett AM, Tang TL, Sugimoto S et al (1994) Protein-tyrosine-phosphatase SHPTP2 couples platelet-derived growth factor receptor beta to Ras. Proc Natl Acad Sci USA 91:7335–7339. https://doi.org/10.1073/pnas.91.15.7335

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chan RJ, Feng G-S (2006) PTPN11 is the first identified proto-oncogene that encodes a tyrosine phosphatase. Blood 109:862–867. https://doi.org/10.1182/blood-2006-07-028829

    Article  CAS  PubMed  Google Scholar 

  7. Holgado-Madruga M, Emlet DR, Moscatello DK, Godwin AK, Wong AJ (1996) A Grb2-associated docking protein in EGF- and insulin-receptor signalling. Nature 379(6565):560–564. https://doi.org/10.1038/379560a0

    Article  CAS  PubMed  Google Scholar 

  8. Kouhara H, Hadari YR, Spivak-Kroizman T, Schilling J, Bar-Sagi D, Lax I, Schlessinger J (1997) A lipid-anchored Grb2-binding protein that links FGF-receptor activation to the Ras/MAPK signaling pathway. Cell 89(5):693–702

    Article  CAS  PubMed  Google Scholar 

  9. Xu D, Wang S, Yu WM et al (2010) A germline gain-of-function mutation in Ptpn11 (Shp-2) phosphatase induces myeloproliferative disease by aberrant activation of hematopoietic stem cells. Blood 116:3611–3621. https://doi.org/10.1182/BLOOD-2010-01-265652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mohi MG, Neel BG (2007) The role of Shp2 (PTPN11) in cancer. Curr Opin Genet Dev 17(1):23–30

    Article  CAS  PubMed  Google Scholar 

  11. Bard-Chapeau EA, Li S, Ding J, Zhang SS, Zhu HH, Princen F, Fang DD, Han T, Bailly-Maitre B, Poli V, Varki NM, Wang H, Feng GS (2011) Ptpn11/Shp2 acts as a tumor suppressor in hepatocellular carcinogenesis. Cancer Cell 19(5):629–639. https://doi.org/10.1016/j.ccr.2011.03.023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yang W, Wang J, Moore DC, Liang H, Dooner M, Wu Q, Terek R, Chen Q, Ehrlich MG, Quesenberry PJ, Neel BG (2013) Ptpn11 deletion in a novel progenitor causes metachondromatosis by inducing hedgehog signalling. Nature 499(7459):491–495. https://doi.org/10.1038/nature12396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Selvaraj C, Chandra I, Singh SK (2022) Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 26:1893–1913. https://doi.org/10.1007/s11030-021-10326-z

    Article  CAS  PubMed  Google Scholar 

  14. Mao J, Akhtar J, Zhang X et al (2021) Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience 24(9):103052. https://doi.org/10.1016/J.ISCI.2021.103052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:1–13. https://doi.org/10.1038/srep42717

    Article  Google Scholar 

  16. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20(23):2839–2860. https://doi.org/10.2174/09298673113209990001

    Article  CAS  PubMed  Google Scholar 

  17. Tripathi RKP, Ayyannan SR (2021) Emerging chemical scaffolds with potential SHP2 phosphatase inhibitory capabilities – A comprehensive review. Chem Biol Drug Des 97:721–773. https://doi.org/10.1111/cbdd.13807

    Article  CAS  PubMed  Google Scholar 

  18. Mitra R, Ayyannan SR (2021) Small-molecule inhibitors of Shp2 phosphatase as potential chemotherapeutic agents for glioblastoma: a minireview. ChemMedChem 16:777–787. https://doi.org/10.1002/cmdc.202000706

    Article  CAS  Google Scholar 

  19. Yuan X, Bu H, Zhou J et al (2020) Recent advances of SHP2 inhibitors in cancer therapy: current development and clinical application. J Med Chem 63:11368–11396. https://doi.org/10.1021/acs.jmedchem.0c00249

    Article  CAS  PubMed  Google Scholar 

  20. Wang W-L, Chen X-Y, Gao Y et al (2017) Benzo[c][1,2,5]thiadiazole derivatives: a new class of potent Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) inhibitors. Bioorg Med Chem Lett 27:5154–5157. https://doi.org/10.1016/j.bmcl.2017.10.059

    Article  CAS  PubMed  Google Scholar 

  21. Lawrence HR, Pireddu R, Chen L et al (2008) Inhibitors of Src homology-2 domain containing protein tyrosine phosphatase-2 (Shp2) based on oxindole scaffolds. J Med Chem 51:4948–4956. https://doi.org/10.1021/jm8002526

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Satheeshkumar R, Zhu R, Feng B et al (2020) Synthesis and biological evaluation of heterocyclic bis-aryl amides as novel Src homology 2 domain containing protein tyrosine phosphatase-2 (SHP2) inhibitors. Bioorg Med Chem Lett 30:7170. https://doi.org/10.1016/j.bmcl.2020.127170

    Article  CAS  Google Scholar 

  23. Geronikaki A, Eleftheriou P, Vicini P et al (2008) 2-Thiazolylimino/heteroarylimino-5-arylidene-4-thiazolidinones as new agents with SHP-2 inhibitory action. J Med Chem 51:5221–5228. https://doi.org/10.1021/jm8004306

    Article  CAS  PubMed  Google Scholar 

  24. Xie J, Si X, Gu S et al (2017) Allosteric inhibitors of SHP2 with therapeutic potential for cancer treatment. J Med Chem 60:10205–10219. https://doi.org/10.1021/acs.jmedchem.7b01520

    Article  CAS  PubMed  Google Scholar 

  25. Bagdanoff JT, Chen Z, Acker M et al (2019) Optimization of fused bicyclic allosteric SHP2 inhibitors. J Med Chem 62:1781–1792. https://doi.org/10.1021/acs.jmedchem.8b01725

    Article  CAS  PubMed  Google Scholar 

  26. Wu J, Li W, Zheng Z et al (2021) Design, synthesis, biological evaluation, common feature pharmacophore model and molecular dynamics simulation studies of ethyl 4-(phenoxymethyl)-2-phenylthiazole-5-carboxylate as Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) inhibitors. J Biomol Struct Dyn 39:1174–1188. https://doi.org/10.1080/07391102.2020.1726817

    Article  CAS  PubMed  Google Scholar 

  27. Yu Z-H, Chen L, Wu L et al (2011) Small molecule inhibitors of SHP2 tyrosine phosphatase discovered by virtual screening. Bioorg Med Chem Lett 21:4238–4242. https://doi.org/10.1016/j.bmcl.2011.05.078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang X, He Y, Liu S et al (2010) Salicylic acid based small molecule inhibitor for the oncogenic SRC homology-2 domain containing protein tyrosine phosphatase-2 (SHP2). J Med Chem 53:2482–2493. https://doi.org/10.1021/jm901645u

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Landrum G (2016) RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org/docs/. Accessed 1 Apr 2023

  30. Li S, Ding Y, Chen M et al (2021) HDAC3i-finder: a machine learning-based computational tool to screen for HDAC3 inhibitors. Mol Inf 40:2000105. https://doi.org/10.1002/minf.202000105

    Article  CAS  Google Scholar 

  31. Albadr MA, Tiun S, Ayob M, AL-Dhief F (2020) Genetic algorithm based on natural selection theory for optimization problems. Symmetry 12(11):1758. https://doi.org/10.3390/sym12111758

    Article  Google Scholar 

  32. Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemom 6:267–281. https://doi.org/10.1002/CEM.1180060506

    Article  CAS  Google Scholar 

  33. Frazier, P.I. (2018). A Tutorial on Bayesian Optimization, arXiv:1807.02811. https://doi.org/10.48550/arXiv.1807.02811

  34. Refaeilzadeh P, Tang L, Liu H (2009) Cross-Validation. Encyclopedia of Database Systems 532–538. https://doi.org/10.1007/978-0-387-39940-9_565

  35. Furcht CM, Buonato JM, Skuli N et al (2014) Multivariate signaling regulation by SHP2 differentially controls proliferation and therapeutic response in glioma cells. J Cell Sci 127:3555–3567. https://doi.org/10.1242/JCS.150862/259148/AM/MULTIVARIATE-SIGNALING-REGULATION-BY-SHP2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang Z, Li Y, Yin F, Chan RJ (2008) Activating PTPN11 mutants promote hematopoietic progenitor cell-cycle progression and survival. Exp Hematol 36(10):1285–1296. https://doi.org/10.1016/j.exphem.2008.04.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  38. Nagai J, Imamura M, Sakagami H, Uesawa Y (2019) QSAR prediction model to search for compounds with selective cytotoxicity against oral cell cancer. Medicines 6:45. https://doi.org/10.3390/medicines6020045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36(22):3219–3228. https://doi.org/10.1016/0040-4020(80)80168-2

    Article  CAS  Google Scholar 

  40. Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci 39(5):868–873. https://doi.org/10.1021/ci990307l

    Article  CAS  Google Scholar 

  41. Riniker S, Landrum GA (2013) Similarity maps—a visualization strategy for molecular fingerprints and machine-learning methods. Journal of Cheminformatics 5:43. https://doi.org/10.1186/1758-2946-5-43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pearlman RS, Smith KM (1998) Novel software tools for chemical diversity. Perspect Drug Discov Des 9:339–353. https://doi.org/10.1023/A:1027232610247

    Article  Google Scholar 

Download references

Acknowledgements

NA is thankful to the Indian Institute of Technology (Banaras Hindu University), Varanasi, for providing a teaching assistantship. The authors are thankful to Mr. R Mitra and Mr. J Lavudi for their support in the data collection. The authors also gratefully acknowledge the Centre for Computing and Information Services (CCIS), Indian Institute of Technology (Banaras Hindu University), Varanasi, for their outstanding computational assistance.

Author information

Authors and Affiliations

  1. Pharmaceutical Chemistry Research Laboratory II, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, UP, 221005, India

    Nilanjan Adhikari & Senthil Raja Ayyannan

Authors
  1. Nilanjan Adhikari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Senthil Raja Ayyannan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, review and editing: Senthil Raja Ayyannan; Investigation and original draft preparation: Nilanjan Adhikari; Data analysis: Nilanjan Adhikari and Senthil Raja Ayyannan.

Corresponding author

Correspondence to Senthil Raja Ayyannan.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 134 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adhikari, N., Ayyannan, S.R. Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10710-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-023-10710-x

Keywords

Search

Navigation