ABSTRACT
Objectives: Diabetes has become a leading cause of mortality in both developed and developing countries, impacting a growing number of individuals worldwide. As the prevalence of the disease continues to rise, researchers have diligently worked towards developing accurate diabetes prediction models. The primary aim of this study is to utilize a diverse set of machine learning algorithms to detect the presence of diabetes, particularly in females, at an early stage. By leveraging these methods, this research seeks to provide physicians with valuable tools to identify the disease early, enabling timely interventions and improving patient outcomes. Methods: In this study, some state-of-the-art machine learning techniques, such as random forest classifiers with gridsearchCV, XGBoost, NGBoost, Bagging, LightGBM, and AdaBoost classifiers, were employed. These models were chosen as the base layer of our proposed stacked ensemble model because of their high accuracy. Before feeding the data into the models, the dataset was preprocessed to ensure optimal performance and obtain improved results. Results: The accuracy achieved in this study was 92.91%, which demonstrates its competitiveness with the existing approaches. Moreover, the utilization of the Shapley additive explanation (SHAP) facilitated the interpretation of machine learning models. Conclusion: We anticipate that these findings will be beneficial to healthcare providers, stakeholders, students, and researchers involved in diabetes prediction research and development.
ABSTRACT
Coronavirus disease-19 (COVID-19) are deadly and infectious disease that impacts individuals in a variety of ways. Scientists have stepped up their attempts to find an antiviral drug that targets the spike protein (S) of Angiotensin converting enzyme 2 (ACE2) (receptor protein) as a viable therapeutic target for coronavirus. The most recent study examines the potential antagonistic effects of 17 phytochemicals present in the plant extraction of Euphorbia neriifolia on the anti-SARS-CoV-2 ACE2 protein. Computational techniques like molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) investigations, and molecular dynamics (MD) simulation analysis were used to investigate the actions of these phytochemicals. The results of molecular docking studies showed that the control ligand (2-acetamido-2-deoxy-ß-D-glucopyranose) had a binding potential of -6.2 kcal/mol, but the binding potentials of delphin, ß-amyrin, and tulipanin are greater at -10.4, 10.0, and -9.6 kcal/mol. To verify their drug-likeness, the discovered hits were put via Lipinski filters and ADMET analysis. According to MD simulations of the complex run for 100 numbers, delphin binds to the SARS-CoV-2 ACE2 receptor's active region with good stability. In root-mean-square deviation (RMSD) and root mean square fluctuation (RMSF) calculations, delphinan, ß-amyrin, and tulipanin showed reduced variance with the receptor binding domain subunit 1(RBD S1) ACE2 protein complex. The solvent accessible surface area (SASA), radius of gyration (Rg), molecular surface area (MolSA), and polar surface area (PSA) validation results for these three compounds were likewise encouraging. The convenient binding energies across the 100 numbers binding period were discovered by using molecular mechanics of generalized born and surface (MM/GBSA) to estimate the ligand-binding free energies to the protein receptor. All things considered, the information points to a greater likelihood of chemicals found in Euphorbia neriifolia binding to the SARS-CoV-2 ACE2 active site. To determine these lead compounds' anti-SARS-CoV-2 potential, in vitro and in vivo studies should be conducted. How to cite this article: Islam MN, Pramanik MEA, Hossain MA, et al. Identification of Leading Compounds from Euphorbia Neriifolia (Dudsor) Extracts as a Potential Inhibitor of SARS-CoV-2 ACE2-RBDS1 Receptor Complex: An Insight from Molecular Docking ADMET Profiling and MD-simulation Studies. Euroasian J Hepato-Gastroenterol 2023;13(2):89-107.
