Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting

Authors

  • Mohammad Hamim Zajuli Al Faroby Department of Data Science, Faculty Information Technology and Business, Institut Teknologi Telkom Surabaya
  • Siti Amiroch Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Islam Darul ‘Ulum, Lamongan, Indonesia
  • Bernadus Anggo Seno Aji Department of Information Technology, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia
  • Avriono Aritonang Department of Data Science, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

Keywords:

Molecular Fingerprint, Extreme Gradient Boosting, Herbal Compound, Machine Learning, IGF1R

Abstract

Diabetes Mellitus is a serious disease that requires serious treatment. The cause of this disease is due to malfunctions in insulin and insulin-producing organs. One of the proteins that become insulin signaling receptors is IGF1R, which has an important role in activating and maximizing insulin performance. In this study, we aimed to obtain herbal compounds that can activate the function of the IGF1R protein by utilizing compound data in an open database and modeling it using the ensemble method, namely extreme gradient boosting. We found that this method produces the best classification model than with other algorithms. We predicted 844 data for herbal compounds, but only 15 data met the threshold of 0.6. We got one plant from the fifteen herbal compounds, namely Zostera Marine, which was confirmed to have compounds that bind to IGF1R. These compounds have the highest probability value in the classification model that we formed compared to others.

Author Biographies

Mohammad Hamim Zajuli Al Faroby, Department of Data Science, Faculty Information Technology and Business, Institut Teknologi Telkom Surabaya

I completed my master’s degree at the Department of Mathematics, Institut Teknologi Sepuluh Nopember in 2020. My thesis is in the field of bioinformatics. Until now, I have been a lecturer in the data science study program at the Telkom Institute of Technology Surabaya; my research is in Bioinformatics, especially in protein data analysis and drug design

Siti Amiroch, Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Islam Darul ‘Ulum, Lamongan, Indonesia

She is a doctoral student in the mathematics department of ITS. Her interest is in computer science, and his dissertation topic is in the area of bioinformatics.

Bernadus Anggo Seno Aji, Department of Information Technology, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

His research interest in artificial intelligence and data mining

Avriono Aritonang, Department of Data Science, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

Student in the department of Data Science, ITTS.

References

A. Sapra and P. Bhandari, Diabetes Mellitus. StatPearls Publishing, Treasure Island (FL), 2019.

J. B. Cole and J. C. Florez, “Genetics of diabetes mellitus and diabetes complications,†Nat. Rev. Nephrol. 2020 167, vol. 16, no. 7, pp. 377–390, May 2020, doi: 10.1038/s41581-020-0278-5.

O. O. Oguntibeju, “Type 2 diabetes mellitus, oxidative stress and inflammation: examining the links,†Int. J. Physiol. Pathophysiol. Pharmacol., vol. 11, no. 3, p. 45, 2019, [Online]. Available: /pmc/articles/PMC6628012/.

H. D. McIntyre, P. Catalano, C. Zhang, G. Desoye, E. R. Mathiesen, and P. Damm, “Gestational diabetes mellitus,†Nat. Rev. Dis. Prim. 2019 51, vol. 5, no. 1, pp. 1–19, Jul. 2019, doi: 10.1038/s41572-019-0098-8.

E. N. Gonc et al., “Genetic IGF1R defects: new cases expand the spectrum of clinical features,†J. Endocrinol. Investig. 2020 4312, vol. 43, no. 12, pp. 1739–1748, Apr. 2020, doi: 10.1007/S40618-020-01264-Y.

M. Hamim, Z. Al, M. I. Irawan, N. Nyoman, and T. Puspaningsih, “Prediction insulin-protein interactions associated based on ontology genes using extreme gradient boosting and centrality method,†Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Contr, vol. 4, no. 5, pp. 253–262, 2020, doi: https://doi.org/10.22219/kinetik.v5i4.107.

Y. Khajebishak, L. Payahoo, M. Alivand, and B. Alipour, “Punicic acid: A potential compound of pomegranate seed oil in Type 2 diabetes mellitus management,†J. Cell. Physiol., vol. 234, no. 3, pp. 2112–2120, Mar. 2019, doi: 10.1002/JCP.27556.

K. A. Carpenter and X. Huang, “Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review,†Curr. Pharm. Des., vol. 24, no. 28, pp. 3347–3358, Dec. 2018, doi: 10.2174/1381612824666180607124038.

Y. Peng and M. H. Nagata, “An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data,†Chaos, Solitons & Fractals, vol. 139, p. 110055, Oct. 2020, doi: 10.1016/J.CHAOS.2020.110055.

Y. Zhou et al., “Quantitative Structure-Activity Relationship (QSAR) Model for the Severity Prediction of Drug-Induced Rhabdomyolysis by Using Random Forest,†Chem. Res. Toxicol., vol. 34, no. 2, pp. 514–521, Feb. 2021, doi: 10.1021/ACS.CHEMRESTOX.0C00347/SUPPL_FILE/TX0C00347_SI_001.ZIP.

A. Capecchi, D. Probst, and J. L. Reymond, “One molecular fingerprint to rule them all: Drugs, biomolecules, and the metabolome,†J. Cheminform., vol. 12, no. 1, pp. 1–15, Jun. 2020, doi: 10.1186/S13321-020-00445-4/FIGURES/8.

M. M. Mysinger, M. Carchia, J. J. Irwin, and B. K. Shoichet, “Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking,†J. Med. Chem., vol. 55, no. 14, pp. 6582–6594, 2012, doi: 10.1021/jm300687e.

S. Kim et al., “PubChem in 2021: new data content and improved web interfaces,†Nucleic Acids Res., vol. 49, no. D1, pp. D1388–D1395, Jan. 2021, doi: 10.1093/NAR/GKAA971.

M. Bagherian, E. Sabeti, K. Wang, M. A. Sartor, Z. Nikolovska-Coleska, and K. Najarian, “Machine learning approaches and databases for prediction of drug–target interaction: a survey paper,†Brief. Bioinform., vol. 22, no. 1, pp. 247–269, Jan. 2021, doi: 10.1093/BIB/BBZ157.

Y. Y. S. Rahayu, T. Araki, and D. Rosleine, “Factors affecting the use of herbal medicines in the universal health coverage system in Indonesia,†J. Ethnopharmacol., vol. 260, p. 112974, Oct. 2020, doi: 10.1016/J.JEP.2020.112974.

P. I. Koukos, M. Réau, and A. M. J. J. Bonvin, “Shape-Restrained Modeling of Protein-Small-Molecule Complexes with High Ambiguity Driven DOCKing,†J. Chem. Inf. Model., vol. 61, no. 9, pp. 4807–4818, Sep. 2021, doi: 10.1021/ACS.JCIM.1C00796/SUPPL_FILE/CI1C00796_SI_002.XLSX.

N. R. Das, S. P. Mishra, and P. G. R. Achary, “Evaluation of molecular structure based descriptors for the prediction of pEC50(M) for the selective adenosine A2A Receptor,†J. Mol. Struct., vol. 1232, p. 130080, May 2021, doi: 10.1016/J.MOLSTRUC.2021.130080.

H. Kuswanto, R. Y. Nurhidayah, and H. Ohwada, “Comparison of Feature Selection Methods to Classify Inhibitors in DUD-E Database,†in Procedia Computer Science, Jan. 2018, vol. 144, pp. 194–202, doi: 10.1016/j.procs.2018.10.519.

A. Salazar, L. Vergara, and G. Safont, “Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets,†Expert Syst. Appl., vol. 163, p. 113819, Jan. 2021, doi: 10.1016/J.ESWA.2020.113819.

A. Fitriawan, I. Wasito, A. F. Syafiandini, M. Amien, and A. Yanuar, “Deep belief networks using hybrid fingerprint feature for virtual screening of drug design,†in 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016, Mar. 2017, pp. 417–420, doi: 10.1109/ICACSIS.2016.7872737.

A. Capecchi, M. Awale, D. Probst, and J. Reymond, “PubChem and ChEMBL beyond Lipinski,†Mol. Inform., vol. 38, no. 5, p. 1900016, May 2019, doi: 10.1002/minf.201900016.

K. Dührkop et al., “SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information,†Nat. Methods, vol. 16, no. 4, pp. 299–302, Apr. 2019, doi: 10.1038/S41592-019-0344-8.

S. Kim, P. A. Thiessen, E. E. Bolton, and S. H. Bryant, “PUG-SOAP and PUG-REST: Web services for programmatic access to chemical information in PubChem,†Nucleic Acids Res., vol. 43, no. W1, pp. W605–W611, 2015, doi: 10.1093/NAR/GKV396.

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,†in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, vol. 13-17-Augu, pp. 785–794, doi: 10.1145/2939672.2939785.

M. Rahman, Y. Cao, X. Sun, B. Li, and Y. Hao, “Deep pre-trained networks as a feature extractor with XGBoost to detect tuberculosis from chest X-ray,†Comput. Electr. Eng., vol. 93, p. 107252, Jul. 2021, doi: 10.1016/J.COMPELECENG.2021.107252.

M. R. Mohammadi et al., “Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state,†Sci. Reports 2021 111, vol. 11, no. 1, pp. 1–20, Sep. 2021, doi: 10.1038/s41598-021-97131-8.

R. R. Syahdi, J. T. Iqbal, A. Munim, and A. Yanuar, “HerbalDB 2.0: Optimization of construction of three-dimensional chemical compound structures to update Indonesian medicinal plant database,†Pharmacogn. J., vol. 11, no. 6, pp. 1189–1194, Jan. 2019, doi: 10.5530/PJ.2019.11.184.

S. Bagui and K. Li, “Resampling imbalanced data for network intrusion detection datasets,†J. Big Data, vol. 8, no. 1, pp. 1–41, Dec. 2021, doi: 10.1186/S40537-020-00390-X/TABLES/49.

R. Singh et al., “Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods,†Chem. Biol. Drug Des., vol. 98, no. 6, pp. 1079–1097, Dec. 2021, doi: 10.1111/CBDD.13965.

R. Couronné, P. Probst, and A. L. Boulesteix, “Random forest versus logistic regression: A large-scale benchmark experiment,†BMC Bioinformatics, vol. 19, no. 1, 2018, doi: 10.1186/s12859-018-2264-5.

N. K. Hepler, A. Bowman, R. E. Carey, and D. J. Cosgrove, “Expansin gene loss is a common occurrence during adaptation to an aquatic environment,†Plant J., vol. 101, no. 3, pp. 666–680, Feb. 2020, doi: 10.1111/TPJ.14572.

Downloads

Published

2022-09-30

Issue

Vol. 16 No. 3 (2022): September 2022

Section

Computational Intelligence

License

Authors who publish with Jurnal Informatika (JIFO) agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

 

 Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.