Integrating QSAR Analysis and Machine Learning To Explore the Antidiabetic Potential of Natural Compounds

Abstract

This study explores the antidiabetic potential of 72 natural compounds using molecular descriptors and QSAR modeling combined with machine learning techniques. The dataset includes 11 experimentally obtained compounds and 61 from the literature, characterized by their IC50 values indicating 50% inhibition of α-glucosidase enzyme activity. Molecular descriptors were generated using ChemAxon’s MarvinSketch and PADEL software, narrowing down over 3000 descriptors to 23 relevant features. Statistical analysis revealed significant multicollinearity among variables, necessitating the application of non-linear machine learning models, namely Random Forest and Gradient Boosting. These models demonstrated predictive capabilities with R² values of 0.7751 and 0.8066, respectively, and highlighted molecular weight and the number of heteroatoms in ring structures as critical features influencing IC50 values. Despite the dataset's variability and limited size, the study underscores the potential of integrating QSAR and machine learning approaches to effectively predict the antidiabetic activity of natural compounds. The findings provide valuable insights for advancing computational methods in drug discovery. © 2025 by the authors.