PMID- 34904009
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211215
IS  - 1735-0328 (Print)
IS  - 1726-6890 (Electronic)
IS  - 1726-6882 (Linking)
VI  - 20
IP  - 3
DP  - 2021 Summer
TI  - Response Surface Study on Molecular Docking Simulations of Citalopram and 
      Donepezil as Potent CNS Drugs.
PG  - 560-576
LID - 10.22037/ijpr.2020.113644.14409 [doi]
AB  - 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.
FAU - Alikhani, Radin
AU  - Alikhani R
AD  - Students Research Committee, School of Pharmacy, Ardabil University of Medical 
      Sciences, Ardabil, Iran.
FAU - Ebadi, Ahmad
AU  - Ebadi A
AD  - Department of Medicinal Chemistry, School of Pharmacy, Hamadan University of 
      Medical Sciences, Hamadan, Iran.
AD  - Medicinal Plants and Natural Products Research Center, Hamadan University of 
      Medical Sciences, Hamadan, Iran.
FAU - Karami, Pari
AU  - Karami P
AD  - Biosensor Sciences and Technologies Research Center, Ardabil University of 
      Medical Sciences, Ardabil, Iran.
FAU - Shahbipour, Sara
AU  - Shahbipour S
AD  - Department of Medicinal Chemistry, School of Pharmacy, Ardabil University of 
      Medical Sciences, Ardabil, Iran.
FAU - Razzaghi-Asl, Nima
AU  - Razzaghi-Asl N
AD  - Department of Medicinal Chemistry, School of Pharmacy, Ardabil University of 
      Medical Sciences, Ardabil, Iran.
AD  - Pharmaceutical Sciences Research Center, Aradabil University of Medical Sciences, 
      Ardabil, Iran.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Iran J Pharm Res
JT  - Iranian journal of pharmaceutical research : IJPR
JID - 101208407
PMC - PMC8653677
OTO - NOTNLM
OT  - Binding
OT  - Central nervous system
OT  - Citalopram
OT  - Donepezil
OT  - Response Surface
OT  - Target
EDAT- 2021/12/15 06:00
MHDA- 2021/12/15 06:01
PMCR- 2021/06/01
CRDT- 2021/12/14 08:22
PHST- 2021/12/14 08:22 [entrez]
PHST- 2021/12/15 06:00 [pubmed]
PHST- 2021/12/15 06:01 [medline]
PHST- 2021/06/01 00:00 [pmc-release]
AID - 10.22037/ijpr.2020.113644.14409 [doi]
PST - ppublish
SO  - Iran J Pharm Res. 2021 Summer;20(3):560-576. doi: 
      10.22037/ijpr.2020.113644.14409.