Document Type: Research article
Department of Medicinal Chemistry, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
Department of Medicinal Chemistry, School of Pharmacy, Hamadan University of Medical Sciences, Hamadan, Iran
Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Biosensors and Bioelectronics Research Center Organization Ardabil University of Medical Sciences, Ardabil, Iran
Computer aided drug design provides broad structural modifications on evolving bioactive molecules without immediate requirement for observing synthetic restraints or tedious protocols. Subsequently, most promising guidelines with regard to synthetic and biological resources may be focused through upcoming steps. Molecular docking is a common in silico drug design technique since it predicts ligand-receptor interaction modes and associated binding affinities. Despite several advantages and historical results, current docking simulations suffer serious constraints in estimating accurate ligand-receptor binding affinities. Response surface method (RSM) is an efficient statistical approach for modelling and optimization of various pharmaceutical systems. With the aim of unveiling 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 best combination of factor levels and considering factor interactions. Results of current study indicated that application of RSM in molecular docking simulations may lead to optimized docking protocols with more stable estimates of ligand-target interactions and hence better correlation of in silico in vitro data.