Quantitative structure-activity relationship study of some angiotensin-converting enzyme inhibitor drugs in the treatment of hypertension based on Monte Carlo optimization method

Introduction: Hypertension is a severe cardiovascular disease affecting human health, and its control and prevention are crucial for global health. Angiotensin-converting-enzyme inhibitors are primarily used in treating this condition. The aim of this work was to develop quantitative structure-activity relationship (QSAR) models to predict the activity of some chemical compounds as angiotensin I-converting enzyme inhibitors using CORAL software. Materials & Methods: In this study, quantitative structure-activity relationship to predict the inhibitory activity of the data set containing 255 angiotensin-converting enzyme inhibitor compounds based on the algorithm Monte Carlo was studied. The input file of CORAL software contains the structures of compounds with SMILES symbols along with their inhibitory activity, which were randomly divided into four sets, including active training, passive training, calibration and validation sets. The whole data set is randomly divided into three splits, and an individual model was created for each division. Results: A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs is employed to construct QSAR models. Four target functions, i.e., TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.3 and WCII = 0), TF2 (WIIC = 0 and WCII = 0.3), and TF3 (WIIC = WCII = 0.3), are employed to build 12 QSAR models. Conclusion: The study analyzed experimental data on chemical compounds as inhibitors of angiotensin I-converting enzyme. The QSAR model of split 2 calculated by TF3 was found to be the best model, and it identified structural attributes responsible for the increase and decrease of inhibitory activity.