Integrated Exploration of Pyranocoumarin Derivatives as Synergistic Inhibitors of Dual-target for Mpro and PLpro Proteins of SARS-CoV-2 through Molecular Docking, ADMET Analysis, and Molecular Dynamics Simulation.

AIMS: This study aimed to explore the potential of natural anticoagulant compounds as synergistic inhibitors of the main protease (Mpro) and papain-like protease (PLpro) of SARS-CoV-2 and find effective therapies against SARS-CoV-2 by investigating the inhibitory effects of natural anticoagulant compounds on key viral proteases. OBJECTIVE: The objectives of this study were to conduct rigorous virtual screening and molecular docking analyses to evaluate the binding affinities and interactions of selected anticoagulant compounds with Mpro and PLpro, to assess the pharmacokinetic and pharmacodynamic profiles of the compounds to determine their viability for therapeutic use, and to employ molecular dynamics simulations to understand the stability of the identified compounds over time. METHODS: In this study, a curated collection of natural anticoagulant compounds was conducted. Virtual screening and molecular docking analyses were performed to assess binding affinities and interactions with Mpro and PLpro. Furthermore, pharmacokinetic and pharmacodynamic analyses were carried out to evaluate absorption, distribution, metabolism, and excretion profiles. Molecular dynamics simulations were performed to elucidate compound stability. RESULTS: Natural compounds exhibiting significant inhibitory activity against Mpro and PLpro were identified. A dual-target approach was established as a promising strategy for attenuating viral replication and addressing coagulopathic complications associated with SARS-CoV-2 infection. CONCLUSION: The study lays a solid foundation for experimental validation and optimization of identified compounds, potentially leading to the development of precise treatments for SARS-CoV-2.

Targeting Fructosamine Oxidase (Amadoriase II) in Aspergillus fumigatus: Comprehensive Virtual Screening, ADMET Analysis, and Molecular Dynamics Simulation of Triazole Derivatives.

INTRODUCTION: Aspergillus fumigatus, a significant fungal pathogen, poses a threat to human health, especially in immunocompromised individuals. Addressing the need for novel antifungal strategies, this study employs virtual screening to identify potential inhibitors of Fructosamine oxidase, also known as Amadoriase II, a crucial enzyme in A. fumigatus (PDB ID: 3DJE). METHOD: Virtual screening of 81,197 triazole derivatives was subjected to computational analysis, aiming to pinpoint molecules with high binding affinity to the active site of Fructosamine oxidase. Subsequently, an in-depth ADMET analysis assessed the pharmacokinetic properties of lead compounds, ensuring their viability for further development. Molecular dynamics simulations were performed to evaluate the stability of top-ranked compounds over time. RESULTS: The results unveil a subset of triazole derivatives displaying promising interactions, suggesting their potential as inhibitors for further investigation. CONCLUSION: This approach contributes to the development of targeted antifungal agents, offering a rational starting point for experimental validation and drug development against Aspergillus fumigatus infections.

Unveiling Moroccan Nature's Arsenal: A Computational Molecular Docking, Density Functional Theory, and Molecular Dynamics Study of Natural Compounds against Drug-Resistant Fungal Infections.

Candida albicans and Aspergillus fumigatus are recognized as significant fungal pathogens, responsible for various human infections. The rapid emergence of drug-resistant strains among these fungi requires the identification and development of innovative antifungal therapies. We undertook a comprehensive screening of 297 naturally occurring compounds to address this challenge. Using computational docking techniques, we systematically analyzed the binding affinity of each compound to key proteins from Candida albicans (PDB ID: 1EAG) and Aspergillus fumigatus (PDB ID: 3DJE). This rigorous in silico examination aimed to unveil compounds that could potentially inhibit the activity of these fungal infections. This was followed by an ADMET analysis of the top-ranked compound, providing valuable insights into the pharmacokinetic properties and potential toxicological profiles. To further validate our findings, the molecular reactivity and stability were computed using the DFT calculation and molecular dynamics simulation, providing a deeper understanding of the stability and behavior of the top-ranking compounds in a biological environment. The outcomes of our study identified a subset of natural compounds that, based on our analysis, demonstrate notable potential as antifungal candidates. With further experimental validation, these compounds could pave the way for new therapeutic strategies against drug-resistant fungal pathogens.

Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study.

The rising prevalence of diabetes necessitates the development of novel drugs, especially given the side effects associated with current medications like Acarbose and Voglibose. A series of 36 Hydrazinyl thiazole-linked indenoquinoxaline derivatives with notable activity against alpha-amylase were studied. To create a molecular model predicting alpha-amylase activity, a QSAR study was performed on these compounds. Molecular descriptors were calculated using Chem3D and Gaussian software and then correlated with their IC50 biological activities to form a dataset. This model data was refined using PCA and modeled with MLR. The model's performance was statistically verified (R2 =0.800; Radj2 = 0.767; Rcv2 = 0.651) and its applicability domain was defined. It was predicted to possess high predictive power (Rtest2  = 0.872). Based on this, new compounds were proposed, and their activities were predicted using the developed model. Additionally, their binding ability to the biological target was studied through molecular docking and dynamics. Their pharmacokinetics were also evaluated using ADMET predictions. Two designed compounds named AE and AB emerged as particularly promising, displaying properties that suggest substantial therapeutic potential and they can form stable complexes into the binding pocket of alpha-amylase enzyme.Communicated by Ramaswamy H. Sarma.

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation.

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using 'PaDEL' and 'ChemDes' software, and then redundant and non-significant descriptors were eliminated by a module in 'QSARINS ver. 2.2.2'. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.