QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods

  • I Kadek Andrean Pramana Putra Pramana Telkom University
  • Reza Rendian Septiawan Telkom University
  • Isman Kurniawan Telkom University
Keywords: Diabetes, Diacylglycerol Acyltransferase-1, Particle Swarm Optimization, QSAR, Support Vector Machine

Abstract

Diabetes mellitus is a chronic disease that can occur in anyone. Up until now, there are no specific drugs have been found which can completely cure diabetes. One of the possible steps to treat diabetes mellitus is by inhibiting the growth of the Diacylglycerol Acyltransferase-1 (DGAT-1) enzyme. This study aims to build a QSAR model on DGAT-1 inhibitors as anti-diabetic using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). Acyl-CoA: DGAT1 is a microsomal enzyme in lipogenesis which is increased in metabolically active cells to meet nutrient requirements. Microsomal enzymes that have an important in the triglyceride-synthesis process of 1,2-diacylglycerol by-catalyzing-acyl-coa-dependent-acylations as anti-diabetics. The dataset used in this study consists of 228 samples containing molecular structures and their inhibitor activities. We reduce the number of features by removing features with a standard deviation less than the threshold value, followed by the PSO algorithm. The best-predicted result is obtained through the implementation of SVM with RBF kernel, with the score of and are 0.75 and 0.67, respectively.

Downloads

Download data is not yet available.

References

R. Joddy, S. Putra, A. Achmad, and H. Rachma, “Pharmaceutical Journal Of Indonesia Kejadian Efek Samping Potensial Terapi Obat Anti Diabetes Pasien Diabetes Melitus Berdasarkan Algoritma Naranjo,” PHARMACEUTICAL JOURNAL OF INDONESIA, vol. 2017, no. 2, pp. 46–46, 2017, [Online]. Available: http://.pji.ub.ac.id

S. Pangribowo, “Infodatin-2020-Diabetes-Melitus,” Pusat Data dan Informasi Kementrian Kesehatan RI, pp. 1–10, 2020.

“IDF Diabetes Atlas Ninth edition 2019,” 2019. doi: 10.1016/S0140-6736(55)92135-8.

P. Kumar, A. Kumar, and J. Sindhu, “In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method,” Taylor Francis Online, vol. 30, no. 8, pp. 525–541, Jul. 2019, doi: 10.1080/1062936X.2019.1629998.

M. R. Keyvanpour and Shirzad Mehrnoush Barani, “An Analysis of QSAR Research Based on Machine Learning Concepts,” PubMed, vol. 18, no. 1, pp. 17–30, 2021, doi: 10.2174/1570163817666200316104404.

U. Saqib and M. I. Siddiqi, “3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents,” SAR and QSAR in Environmental Research, vol. 20, no. 5–6, pp. 519–535, 2009, doi: 10.1080/10629360903278677.

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 and QSAR in Environmental Research, vol. 30, no. 6, pp. 403–416, Jun. 2019, doi: 10.1080/1062936X.2019.1607899.

K. Faghihi, M. Safakish, T. Zebardast, and A. Zarghi, “Molecular Docking and QSAR Study of 2-Benzoxazolinone, Quinazoline and Diazocoumarin Derivatives as Anti-HIV-1 Agents,” Iranian Journal of Pharmaceutical Research, vol. 18, no. 3, pp. 1253–1263, 2019, doi: 10.22037/ijpr.2019.1100746.

I. Kurniawan, M. S. Fareza, and P. Iswanto, “Comfa, molecular docking and molecular dynamics studies on cycloguanil analogues as potent antimalarial agents,” Indonesian Journal of Chemistry, vol. 21, no. 1, pp. 66–76, Feb. 2021, doi: 10.22146/ijc.52388.

W. S. Dharmawan, “Dalam Prediksi Penyakit Jantung,” Jurnal Informatika, Manajemen dan Komputer, vol. 13, no. 2, 2021.

R. Amanullah Hakim, I. Kurniawan, and A. Aditsania, “Studi QSAR pada Senyawa Disulfida Aromatik sebagai Inhibitor SARS-CoV Mpro dengan Menggunakan Metode Genetic Algorithm-Support Vector Machine,” Jurnal Tugas Akhir Fakultas Informatika, 2022.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” International Conference on Neural Networks, vol. 4, no. 0, pp. 1942–1948, Nov. 1995, doi: 10.1109/icnn.1995.488968.

R. Fahryandi, Y. Sibaroni, and A. Romadhony, “Klasifikasi Cyberbullying Terhadap Tokoh Publik Pada Komentar Sentimen Instagram Dengan Menggunakan Metode Support Vector Machine Dan Optimasi Fitur Berbasis Particle Swarm Optimization,” Telkom University, Bandung, 2021.

A. Nurlaily and M. T. S. R. Pradina Kusumawardani S.T., “Prediksi Diabetes Berdasarkan Faktor Risiko Behavioral Menggunakan Algoritma Support Vector Machine,” Institut Teknologi Sepuluh November, Surabaya, 2018.

N. Dini Maulana, B. Darma Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus: Harum Bakery),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp. 2986–2995, Mar. 2019, [Online]. Available: http://j-ptiik.ub.ac.id

S. Aprilla, M. T. Furqon, and M. A. Fauzi, “Klasifikasi Penyakit Skizofrenia dan Episode Depresi Pada Gangguan Kejiwaan Dengan Menggunakan Metode Support Vector Machine (SVM),” 2018. [Online]. Available: http://j-ptiik.ub.ac.id

N. P. Nanik Hendayanti, I. K. Putu Suniantara, and M. Nurhidayati, “Penerapan Support Vector Regression (Svr) dalam Memprediksi Jumlah Kunjungan Wisatawan Domestik ke Bali,” Jurnal Varian, vol. 3, no. 1, Oct. 2019, doi: https://doi.org/10.30812/varian.v3i1.506.

V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd Edition. New York Berlin Heidelberg: Springer-Verlag, 1995.

H. F. Azmi, K. M. Lhaksmana, and I. Kurniawan, “QSAR Study of Fusidic Acid Derivative as Anti-Malaria Agents by using Artificial Neural Network-Genetic Algorithm,” 2020.

I. Kurniawan, R. Wardhani, M. Rosalinda, and N. Ikhsan, “QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 2, p. 62, Jul. 2021, doi: 10.24843/lkjiti.2021.v12.i02.p01.

Published
2022-10-01
How to Cite
Pramana, I. K. A. P. P., Reza Rendian Septiawan, & Isman Kurniawan. (2022). QSAR Study on Diacylgycerol Acyltransferase-1 (DGAT-1) Inhibitor as Anti-diabetic using PSO-SVM Methods. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(5), 735 - 741. https://doi.org/10.29207/resti.v6i5.4294
Section
Artikel Teknologi Informasi