DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

  • Reza Rendian Septiawan Telkom University
  • Bambang Hadi Prakoso Telkom University
  • Isman Kurniawan Telkom University
Keywords: dipeptidyl peptidase IV inhibitor, particle swarm optimization, quantitative structure activity relationship, support vector machine


Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.



Download data is not yet available.


American Diabetes Association, “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 27 Suppl 1, pp. S5–S10, Jan. 2004.

doi: 10.2337/diacare.27.2007.s5.

“Standards of Medical Care in Diabetes—2015 Abridged for Primary Care Providers,” Clin. Diabetes Publ. Am. Diabetes Assoc., vol. 33, no. 2, pp. 97–111, Apr. 2015.

doi: 10.2337/diaclin.33.2.97.

S. Anazawa, “[Gestational diabetes mellitus],” Nihon Rinsho Jpn. J. Clin. Med., vol. 73, no. 12, pp. 2015–2021, Dec. 2015.

B. D. Green, P. R. Flatt, and C. J. Bailey, “Dipeptidyl peptidase IV (DPP IV) inhibitors: a newly emerging drug class for the treatment of type 2 diabetes,” Diab. Vasc. Dis. Res., vol. 3, no. 3, pp. 159–165, Dec. 2006.

doi: 10.3132/dvdr.2006.024.

M. Bobbi, K. Nasution, S. Suryadi, and R. Watrianthos, “Model Pengenalan Suara Teks Bebas Menggunakan Algoritma Support Vector Machine,” Jurnal Media Informatika Budidarma, vol. 4, no. 4, pp. 1249–1255, 2020, doi: 10.30865/mib.v4i4.2436.

D. Kirpichnikov, S. I. McFarlane, and J. R. Sowers, “Metformin: an update,” Ann. Intern. Med., vol. 137, no. 1, pp. 25–33, Jul. 2002.

doi: 10.7326/0003-4819-137-1-200207020-00009.

E. Kristin, “DIPEPTIDYL PEPTIDASE 4 (DPP-4) INHIBITORS FOR THE TREATMENT OF TYPE 2 DIABETES MELLITUS,” J. Med. Sci. Berk. Ilmu Kedokt., vol. 48, no. 2, Art. no. 2, Dec. 2016.

doi: 10.19106/JMedSci004802201606.

J. J. Holst and C. F. Deacon, “Inhibition of the activity of dipeptidyl-peptidase IV as a treatment for type 2 diabetes,” Diabetes, vol. 47, no. 11, pp. 1663–1670, Nov. 1998.

doi: 10.2337/diabetes.47.11.1663.

P. R. Flatt, C. J. Bailey, and B. D. Green, “Dipeptidyl peptidase IV (DPP IV) and related molecules in type 2 diabetes,” Front. Biosci., vol. 13, no. 10, pp. 3648–3660, May 2008.

doi: 10.2741/2956.

C. F. Deacon, “Dipeptidyl peptidase-4 inhibitors in the treatment of type 2 diabetes: a comparative review,” Diabetes Obes. Metab., vol. 13, no. 1, pp. 7–18, Jan. 2011.

doi: 10.1111/j.1463-1326.2010.01306.x.

X. Yang, M. Li, Q. Su, M. Wu, T. Gu, and W. Lu, “QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes,” Med. Chem. Res., vol. 22, no. 11, pp. 5274–5283, Nov. 2013.

doi: 10.1007/s00044-013-0527-2.

E. Estrada, “On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research,” SAR QSAR Environ. Res., vol. 11, no. 1, pp. 55–73, 2000.

doi: 10.1080/10629360008033229.

A. M. Al-Fakih, Z. Y. Algamal, M. H. Lee, M. Aziz, and H. T. M. Ali, “A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm,” SAR QSAR Environ. Res., vol. 30, no. 6, pp. 403–416, Jun. 2019.

doi: 10.1080/1062936X.2019.1607899.

M. C. Sharma, S. Jain, and R. Sharma, “Trifluorophenyl-based inhibitors of dipeptidyl peptidase-IV as antidiabetic agents: 3D-QSAR COMFA, CoMSIA methodologies,” Netw. Model. Anal. Health Inform. Bioinforma., vol. 7, no. 1, p. 1, Dec. 2017.

doi: 10.1007/s13721-017-0163-8.

C. Jiang, S. Han, T. Chen, and J. Chen, “3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors,” Acta Pharm. Sin. B, vol. 2, no. 4, pp. 411–420, Aug. 2012.

doi: 10.1016/j.apsb.2012.06.007.

B. D. Patel and M. D. Ghate, “3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods,” Med. Chem. Res., vol. 24, no. 3, pp. 1060–1069, Mar. 2015.

doi: 10.1007/s00044-014-1178-7.

U. Saqib and M. I. Siddiqi, “3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents,” SAR QSAR Environ. Res., vol. 20, no. 5–6, pp. 519–535, Jul. 2009.

doi: 10.1080/10629360903278677.

Z. Wang, G. L. Durst, R. C. Eberhart, D. B. Boyd, and Z. B. Miled, “Particle swarm optimization and neural network application for QSAR,” in 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings., Apr. 2004, pp. 194-.

doi: 10.1109/IPDPS.2004.1303214.

H. Nguyen, “Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam,” SN Appl. Sci., vol. 1, no. 4, p. 283, Mar. 2019.

doi: 10.1007/s42452-019-0295-9.

I. Kurniawan, D. Tarwidi, and Jondri, “QSAR modeling of PTP1B inhibitor by using Genetic algorithm-Neural network methods,” J. Phys. Conf. Ser., vol. 1192, p. 012059, Mar. 2019.

doi: 10.1088/1742-6596/1192/1/012059.

J. Benesty, J. Chen, and Y. Huang, “On the Importance of the Pearson Correlation Coefficient in Noise Reduction,” IEEE Trans. Audio Speech Lang. Process., vol. 16, no. 4, pp. 757–765, May 2008.

doi: 10.1109/TASL.2008.919072.

I. Kurniawan, M. Rosalinda, and N. Ikhsan, “Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent,” SAR QSAR Environ. Res., vol. 31, no. 6, pp. 477–492, Jun. 2020.

doi: 10.1080/1062936X.2020.1773534.

M. Zamani, M. Karimi-Ghartemani, N. Sadati, and M. Parniani, “Design of a fractional order PID controller for an AVR using particle swarm optimization,” Control Eng. Pract., vol. 17, no. 12, pp. 1380–1387, Dec. 2009.

doi: 10.1016/j.conengprac.2009.07.005.

Hindawi, “Artificial Intelligence and Its Applications 2014.” https://www.hindawi.com/journals/mpe/2016/3871575/ (accessed Sep. 15, 2022).

Hindawi, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications.” https://www.hindawi.com/journals/mpe/2015/931256/ (accessed Sep. 15, 2022).

“Photonic neural networks and learning machines | IEEE Journals & Magazine | IEEE Xplore.” https://ieeexplore.ieee.org/document/163674 (accessed Sep. 15, 2022).

S. Shamshirband et al., “Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission,” Energy, vol. 67, pp. 623–630, Apr. 2014.

doi: 10.1016/j.energy.2014.01.111.

P. Sihag, P. Jain, and M. Kumar, “Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression,” Model. Earth Syst. Environ., vol. 4, no. 1, pp. 61–68, Apr. 2018.

doi: 10.1007/s40808-017-0410-0.

F. Wang, Z. Zhen, B. Wang, and Z. Mi, “Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting,” Appl. Sci., vol. 8, no. 1, Art. no. 1, Jan. 2018.

doi: 10.3390/app8010028.

S. Xu, B. Lu, M. Baldea, T. F. Edgar, and M. Nixon, “An improved variable selection method for support vector regression in NIR spectral modeling,” J. Process Control, vol. 67, pp. 83–93, Jul. 2018.

doi: 10.1016/j.jprocont.2017.06.001.

Z. Zhong and T. R. Carr, “Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 – Reservoir oil minimum miscibility pressure prediction,” Fuel, vol. 184, pp. 590–603, Nov. 2016.

doi: 10.1016/j.fuel.2016.07.030.

S. M. S. Nugroho, I. A. Budiastuti, and M. Hariadi, “Predicting daily consumer price index using support vector regression method based cloud computing,” in 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), Aug. 2017, pp. 313–318.

doi: 10.1109/ISITIA.2017.8124101.

K. Roy and I. Mitra, “On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design,” Comb. Chem. High Throughput Screen., vol. 14, no. 6, pp. 450–474, Jul. 2011.

doi: 10.2174/138620711795767893.

B. Sepehri and R. Ghavami, “Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides,” SAR QSAR Environ. Res., vol. 30, no. 1, pp. 21–38, Jan. 2019.

doi: 10.1080/1062936X.2018.1545695.

G. Schüürmann, R.-U. Ebert, J. Chen, B. Wang, and R. Kühne, “External Validation and Prediction Employing the Predictive Squared Correlation Coefficient — Test Set Activity Mean vs Training Set Activity Mean,” J. Chem. Inf. Model., vol. 48, no. 11, pp. 2140–2145, Nov. 2008.

doi: 10.1021/ci800253u.

How to Cite
Reza Rendian Septiawan, Bambang Hadi Prakoso, & Isman Kurniawan. (2022). DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 974 - 980. https://doi.org/10.29207/resti.v6i6.4470
Information Technology Articles