Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Integrating QSAR Analysis and Machine Learning To Explore the Antidiabetic Potential of Natural Compounds(AMG Transcend Association, 2025) Sincar, B.; Yalcin, D.; Bayraktar, O.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.Article Recent Developments in the Treatment of Leishmaniasis: Natural Compounds, Drug Targets, in Silico Molecular Docking Approaches, and Nanocarriers(Elsevier B.V., 2025) Gürbüz Çolak, N.Leishmaniasis is a common tropical disease caused by Leishmania protozoa. It affects 0.9 to 1.6 million people, causing 20,000–30,000 deaths annually. There are no effective vaccines, and current treatments have severe side effects. Drug resistance is a major obstacle in treating leishmaniasis. The necessity of drug discovery is indisputable. Natural compounds are promising candidates for drug discovery studies because of their diverse chemical structures and bioactivities. Experimental screening of compound libraries imposes high costs and is time-consuming. The molecular docking approach is beneficial for exploring new therapeutics in silico as it allows the screening of millions of drug candidates. Even if new drug candidates are discovered, delivery of the active ingredient to the target remains controversial. Nanocarriers are promising nanosystems that can address the drawbacks of drug delivery. This chapter focuses on natural compounds as drug candidates, targets, in silico drug discovery, and drug delivery for the treatment of leishmaniasis. © 2025 Elsevier Inc.