Aim: To investigate the binding mode of synthesized adamantly derivatives inside of cholinesterase enzymes using molecular docking simulations. Methods: A series of hybrid compounds containing adamantane and hydrazide moieties was designed and synthesized. Their inhibitory activities against acetylcholinesterase (AChE) and (butyrylcholinesterase) BChE were assessed in vitro. The binding mode of the compounds inside cholinesterase enzymes was investigated using Surflex-Dock package of Sybyl7.3 software. Results: A total of 26 adamantyl derivatives were synthesized. Among them, adamantane-1-carboxylic acid hydrazide had an almost equal inhibitory activity towards both enzymes, whereas 10 other compounds exhibited moderate inhibitory activity against BChE. The molecular docking studies demonstrated that hydrophobic interactions between the compounds and their surrounding residues in the active site played predominant roles, while hydrophilic interactions were also found. When the compounds were docked inside each enzyme, they exhibited stronger interactions with BChE over AChE, possibly due to the larger active site of BChE. The binding affinities of the compounds for BChE and AChE estimated were in agreement with the experimental data. Conclusion:The new adamantly derivatives selectively inhibit BChE with respect to AChE, thus making them good candidates for testing the hypothesis that BChE inhibitors would be more efficient and better tolerated than AChE inhibitors in the treatment of Alzheimer's disease.

Natural alkaloid Physostigmine is one of the most potent pseudo-irreversible inhibitor of Acetylcholinesterase. It was found to accelerate long-term memory process, but due to its short half life and variable bioavailability, has inconsistent clinical efficacy. 3D-QSAR studies based on the comparative molecular field analysis and comparative molecular similarity indices analysis were applied to a set of 40 Physostigmine derivatives which are divided into two classes: A and B. The study was conducted to obtain a highly reliable and extensive dynamic QSAR model based on alignment procedure with co-crystallized Ganstigmine as template. The strategy yielded significant 3D-QSAR models with the cross-validated q(2) values 0.762 and 0.754 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Resulted models were validated by external set of eight compounds yielding high correlation coefficient r(2) values of 0.730 and 0.720 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Furthermore, the analysis of comparative molecular field analysis and comparative molecular similarity indices analysis contour maps within the active site of AChE were conducted in order to understand the interactions between the receptor and the Physostigmine derivatives. This study will facilitate the rational design of more potent Physostigmine compounds which might have better activity and reduce toxicity for the treatment of Alzheimer disease.

BackgroundTremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients.ResultsIn the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46.ConclusionThe combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses.