Abstract
Factor Xa (FXa) enzyme has an important role in the blood coagulation system. Disruption in the enzyme function results in the production of blood clots. Therefore, inhibition of the factor Xa with anticoagulant drugs is an important target in thromboembolic therapy. Experimental design of new drugs is a time-consuming and expensive process. Application of molecular modeling is an essential step for designing new and high yield drugs to achieve better results. In this study, 36 aroylguanidine derivatives that inhibit destructive activities by binding to the FXa enzyme were selected. Quantitative Structure-Activity Relationships (QSAR) model was developed for predicting the IC50 parameter for factor Xa inhibitors. QSAR have been constructed by combining Genetic Algorithms with Multiple Linear Regressions (GA-MLR) and Support Vector Machine (SVM). The correlation coefficient (R2) and root mean square error (RMSE) for GA-MLR are 0.895, 0.142 and for SVM are 0.994, 0.032, respectively. The obtained results indicated that the SVM method is superior over the GA-MLR method. Also, molecular docking was used to examine the results more accurately, in which energy interactions were investigated. Molecular docking indicated the binding relationship between the receptor and the ligand. The results showed that reducing the parameters such as electronegativity, and the size of the atoms, as well as increasing the number of loops and groups attached to the structures, is effective in the model.
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The authors gratefully acknowledge the support of the Institute of Petroleum Engineering (IPE), University of Tehran.
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Ramandi, M., Riahi, S., Rahimi, H. et al. Molecular docking, linear and nonlinear QSAR studies on factor Xa inhibitors. Struct Chem 31, 2023–2040 (2020). https://doi.org/10.1007/s11224-020-01535-7
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DOI: https://doi.org/10.1007/s11224-020-01535-7