Integrated 3D-QSAR, molecular docking, and molecular dynamics simulation studies on 1,2,3-triazole based derivatives for designing new acetylcholinesterase inhibitors

Abstract

Alzheimer’s disease (AD) is a multifactorial and polygenic disease. It is the most prevalent reason for dementia in the aging population. A dataset of twenty-six 1,2,3-triazole-based derivatives previously synthetized and evaluated for acetylcholinesterase inhibitory activity were subjected to the three-dimensional quantitative structure-activity relationship (3D-QSAR) study. Good predictability was achieved for comparative molecular field analysis (CoMFA) (Q 2 = 0.604, R 2 = 0.863, r ext 2 = 0.701) and comparative molecular similarity indices analysis (CoMSIA) (Q 2 = 0.606, R 2 = 0.854, r ext 2 = 0.647). The molecular features characteristics provided by the 3D-QSAR contour plots were quite useful for designing and improving the activity of acetylcholinesterase of this class. Based on these findings, a new series of 1,2,3-triazole based derivatives were designed, among which compound A1 with the highest predictive activity was subjected to detailed molecular docking and compared to the most active compound. The selected compounds were further subjected to 20 ns molecular dynamics (MD) simulations to study the comparative conformation dynamics of the protein after ligand binding, revealing promising results for the designed molecule. Therefore, this study could provide worthy guidance for further experimental analysis of highly effective acetylcholinesterase inhibitors.

Rutin Potentially Binds the Gamma Secretase Catalytic Site, Down Regulates the Notch Signaling Pathway and Reduces Sphere Formation in Colonospheres

“…MD simulations are a common method for determining the stability and interaction pattern of protein-ligand complexes [ 59 , 60 , 61 ]. The GROMACS MD simulation program was used to simulate the unbound gamma secretase catalytic subunit (UGSC), as well as DGSC and RGSC.…”

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

“…As a result, more research is needed to discover new compounds with structural properties suitable for safe drug use that are less toxic and more effective against the growth of cancer cell lines caused by the enzymatic activity of the c-Met protein than currently available drugs. For this reason, based on QSAR modeling, in our current work, we characterize the structures of small molecules based on cyclohexane-1,3-dione and identify the most important structural properties of these molecules that influence their biological activity against NSCLC. , For QSAR modeling, we used a combination of topological, physicochemical, and electronic DFT molecular descriptors that are commonly used for geometrical structural characterization. , The DFT computations were used because of their precision in providing precise indications on the electronic properties of the studied molecules, allowing for the generation of confident QSAR models. − Furthermore, the drug-like and pharmacokinetic absorption, distribution, metabolism, excretion, toxicity (ADMET) properties of the candidate drug molecules will be examined. − On the other hand, we performed molecular docking simulations to evaluate the binding potential of the examined small molecules toward the c-Met protein active pocket. This is due to the importance of this procedure for predicting potential interactions between ligands and active amino acid residue sites inside the target protein receptor pocket. , The interactions between the investigated heterocyclic compounds and c-Met can result in a strong noncovalent binding between the two ends, which can provide a strong inhibition of c-Met protein enzymatic activity.…”

“…For this reason, based on QSAR modeling, in our current work, we characterize the structures of small molecules based on cyclohexane-1,3-dione and identify the most important structural properties of these molecules that influence their biological activity against NSCLC. 28 , 29 For QSAR modeling, we used a combination of topological, physicochemical, and electronic DFT molecular descriptors that are commonly used for geometrical structural characterization. 30 , 31 The DFT computations were used because of their precision in providing precise indications on the electronic properties of the studied molecules, allowing for the generation of confident QSAR models.…”

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

“…CoMFA is based on molecular field analysis and represents real three-dimensional descriptors. 37 CoRSA generates a virtual receptor model by considering the common electrostatic and steric properties of a set of molecules. 31 SOMFA has a similarity in concept with CoMFA and can be applied in three-dimensional QSAR studies.…”