COMPUTATIONAL QSAR-BASED MACHINE LEARNING APPROACH FOR PREDICTING ACTIVITY OF SGLT2 INHIBITORS USING THE KNIME PLATFORM

ADHA DASTU ILLAHI; GATOT FATWANTO HERTONO; ARRY YANUAR

doi:10.22159/ijap.2025v17i1.51726

Authors

ADHA DASTU ILLAHI Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok-16424, Jawa Barat, Indonesia https://orcid.org/0009-0004-9940-7640
GATOT FATWANTO HERTONO Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia Barat, Indonesia
ARRY YANUAR Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok-16424, Jawa Barat, Indonesia

DOI:

https://doi.org/10.22159/ijap.2025v17i1.51726

Keywords:

QSAR, SGLT2 inhibitor, Machine Learning, KNIME, Artificial Intelligent, In silico

Abstract

Objective: This study aims to identify optimal predictive models and key molecular fragments by preparing a dataset and using machine learning techniques within the Konstanz Information Miner (KNIME) platform.

Methods: The human Sodium-glucose Cotransporter 2 (SGLT2) target dataset was obtained from the ChEMBL database and refined by removing salts, incomplete/incorrect data, and duplicates. The data was classified into active and inactive compounds, and fingerprints and descriptors were calculated. Christian Borgelt's Molecular Substructure Miner (MoSS) was employed to identify frequent molecular fragments. Following data partitioning, various ‘classification’ and ‘regression’ machine learning (ML) based Quantitative Structure-Activity Relationship (QSAR) models were developed and evaluated using different techniques, including sensitivity and Mean Squared Error (MSE).

Results: In QSAR classification, the Support Vector Machine (SVM) model demonstrated the best performance with an accuracy of 81.66%, while in QSAR Regression, the Extreme Gradient Boosting (XGB) model exhibited the best coefficient of determination (R²) and Mean Absolute Error (MAE) values of 0.69 and 0.47 respectively. The identification of frequent Molecular Fragments highlighted common characteristics in active SGLT2 inhibitors.

Conclusion: The results of developing these QSAR models indicate that machine learning methods can be effectively used to predict SGLT2 inhibitors virtually, thereby expediting the drug discovery process.

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