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Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold

Yıl 2024, Cilt: 28 Sayı: 4, 1135 - 1151, 28.06.2025

Irina Ghosh Komal Singh Venkatesan Jayaprakash , Sudeepan Jayapalan

Öz

Diabetes is a multifactorial metabolic disorder occurs due to uncontrolled persistent hyperglycaemia. The α-glucosidase enzyme plays an important role in management of diabetes. The α-glucosidase enzyme gets secreted by the brush border cells of small intestine which helps in converting maltose into glucose and thereby inhibiting the enzyme will help in lowering blood glucose level. In the present study, 100 compounds were selected having activity against α-glucosidase enzyme and they were used to build a machine learning based quantitative structure activity relationship model (QSAR). All the compounds were having thiazolidinedione (TZD) as the common nucleus. The molecules selected were divided into training and testing datasets of 80:20 ratio for various model development. The important molecular descriptors which will affect the target were chosen using recursive feature elimination (RFE) algorithm. The predictive models were created using machine learning regression techniques including Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR) and Gradient Boosting Regression (GBR). A comparison-based analysis was done between the various machine learning algorithms. The GBR and RFR gave the best R2 value of 0.9992 and 0.9514 for the training dataset and 0.9414 and 0.8760 for the testing dataset respectively, followed by SVR and DTR. Thus, it concludes that the four-machine learning algorithm generates a highly predictive model for the unique compounds and a superior prediction capability for building a QSAR model for α- glucosidase enzyme inhibitors.

Anahtar Kelimeler

Machine Learning α-glucosidase TZD Bioactivity QSAR

Kaynakça

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Yıl 2024, Cilt: 28 Sayı: 4, 1135 - 1151, 28.06.2025

Irina Ghosh Komal Singh Venkatesan Jayaprakash , Sudeepan Jayapalan

Öz

Kaynakça

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  • [3] Gaba S, Singh G, Monga V. Design, synthesis, and characterization of new thiazolidinedione derivatives as potent _ampersandsignalpha;-glucosidase inhibitors. Pharmaspire. 2021 Oct;13: 182-193. https://www.isfcppharmaspire.com/article_html.php?did=13767&issueno=0.
  • [4] Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine (Baltimore). 2010;38(11): 602-606. https://doi.org/10.1016/j.mpmed.2010.08.007.
  • [5] Reichard P, Pihl M. Mortality and treatment side-effects during long-term ıntensified conventional ınsulin treatment in the Stockholm diabetes ıntervention study. Diabetes. 1994;43(2): 313-317. https://doi.org/10.2337/diab.43.2.313.
  • [6] Hussain F, Khan Z, Jan MS, Ahmad S, Ahmad A, Rashid U, Ullah F, Ayaz M, Sadiq A. Synthesis, in-vitro α-glucosidase inhibition, antioxidant, in-vivo antidiabetic and molecular docking studies of pyrrolidine-2,5-dione and thiazolidine-2,4-dione derivatives. Bioorg Chem. 2019;91:103128. https://doi.org/10.1016/j.bioorg.2019.103128.
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  • [10] Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6-7): 476-488. https://doi.org/10.1002/minf.201000061.
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  • [14] Weaver S, Gleeson MP. The importance of the domain of applicability in QSAR modeling. J Mol Graph Model. 2008;26(8): 1315-1326. https://doi.org/10.1016/j.jmgm.2008.01.002.
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Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Farmasotik Kimya
Bölüm Articles
Yazarlar

Irina Ghosh 0000-0002-1138-7867

Komal Singh 0000-0002-2026-6987

Venkatesan Jayaprakash 0000-0002-9724-4153

Sudeepan Jayapalan 0000-0001-5078-3451

Yayımlanma Tarihi 28 Haziran 2025
Yayımlandığı Sayı Yıl 2024 Cilt: 28 Sayı: 4

Kaynak Göster

APA Ghosh, I., Singh, K., Jayaprakash, V., Jayapalan, S. (2025). Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold. Journal of Research in Pharmacy, 28(4), 1135-1151.
AMA Ghosh I, Singh K, Jayaprakash V, Jayapalan S. Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold. J. Res. Pharm. Temmuz 2025;28(4):1135-1151.
Chicago Ghosh, Irina, Komal Singh, Venkatesan Jayaprakash, ve Sudeepan Jayapalan. “Machine Learning Based QSAR Model for Therapeutically Active Candidate Drugs With Thiazolidinedione (TZD) Scaffold”. Journal of Research in Pharmacy 28, sy. 4 (Temmuz 2025): 1135-51.
EndNote Ghosh I, Singh K, Jayaprakash V, Jayapalan S (01 Temmuz 2025) Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold. Journal of Research in Pharmacy 28 4 1135–1151.
IEEE I. Ghosh, K. Singh, V. Jayaprakash, ve S. Jayapalan, “Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold”, J. Res. Pharm., c. 28, sy. 4, ss. 1135–1151, 2025.
ISNAD Ghosh, Irina vd. “Machine Learning Based QSAR Model for Therapeutically Active Candidate Drugs With Thiazolidinedione (TZD) Scaffold”. Journal of Research in Pharmacy 28/4 (Temmuz 2025), 1135-1151.
JAMA Ghosh I, Singh K, Jayaprakash V, Jayapalan S. Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold. J. Res. Pharm. 2025;28:1135–1151.
MLA Ghosh, Irina vd. “Machine Learning Based QSAR Model for Therapeutically Active Candidate Drugs With Thiazolidinedione (TZD) Scaffold”. Journal of Research in Pharmacy, c. 28, sy. 4, 2025, ss. 1135-51.
Vancouver Ghosh I, Singh K, Jayaprakash V, Jayapalan S. Machine learning based QSAR model for therapeutically active candidate drugs with thiazolidinedione (TZD) scaffold. J. Res. Pharm. 2025;28(4):1135-51.