آنالیز سطح پاسخ در شبیه سازی داکینگ مولکولی سیتالوپرام و دونپزیل

عنوان انگليسی

Response Surface Study on Molecular Docking Simulations of Citalopram and Donepezil as Potent CNS Drugs

خلاصه انگلیسی

Computer-aided drug design provides broad structural modifications to evolving bioactive molecules without an immediate requirement to observe synthetic restraints or tedious protocols. Subsequently, the most promising guidelines with regard to synthetic and biological resources may be focused on upcoming steps. Molecular docking is common in-silico drug design techniques since it predicts ligand-receptor interaction modes and associated binding affinities. Current docking simulations suffer serious constraints in estimating accurate ligand-receptor binding affinities despite several advantages and historical results. Response surface method (RSM) is an efficient statistical approach for modeling and optimization of various pharmaceutical systems. With the aim of unveiling the full potential of RSM in optimizing molecular docking simulations, this study particularly focused on binding affinity prediction of citalopram-serotonin transporter (SERT) and donepezil-acetyl cholinesterase (AChE) complexes. For this purpose, Box-Behnken design of experiments (DOE) was used to develop a trial matrix for simultaneous variations of AutoDock4.2 driven binding affinity data with selected factor levels. Responses of all docking trials were considered as estimated protein inhibition constants with regard to validated data for each drug. The output matrix was subjected to statistical analysis and constructing polynomial quadratic models. Numerical optimization steps to attain ideal docking accuracies revealed that more accurate results might be envisaged through the best combination of factor levels and considering factor interactions. Results of the current study indicated that the application of RSM in molecular docking simulations might lead to optimized docking protocols with more stable estimates of ligand-target interactions and hence better correlation of in-silico in-vitro data.

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