Identification of Potent Acetylcholinesterase Inhibitors as New Candidates for Alzheimer Disease via Virtual Screening, Molecular Docking, Dynamic Simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area Calculations.

Huperzine A (HUP) plays a crucial role in Alzheimer's therapy by enhancing cognitive function through increased cholinergic activity as a reversible acetylcholinesterase (AChE) inhibitor. Despite some limitations being seen in AChE inhibitors, ongoing research remains dedicated to finding innovative and more effective treatments for Alzheimer's disease. To achieve the goal of the discovery of potential HUP analogues with improved physicochemical properties, less toxic properties, and high biological activity, many in silico methods were applied. Based on the acetylcholinesterase-ligand complex, an e-pharmacophore model was developed. Subsequently, a virtual screening involving a collection of 1762 natural compounds, sourced from the PubChem database, was performed. This screening yielded 131 compounds that exhibited compatibility with the established pharmacophoric hypothesis. These selected ligands were then subjected to molecular docking within the active site of the 4EY5 receptor. As a result, we identified four compounds that displayed remarkable docking scores and exhibited low free binding energy to the target. These top four compounds, CID_162895946, CID_44461278, CID_44285285, and CID_81108419, were submitted to ADMET prediction and molecular dynamic simulations, yielding encouraging findings in terms of their pharmacokinetic characteristics and stability. Finally, the molecular dynamic simulation, cross-dynamic correlation matrix, free energy landscape, and MM-PBSA calculations demonstrated that two ligands from the selected ligands formed very resilient complexes with the enzyme acetylcholinesterase, with significant binding affinity. Therefore, these two compounds are recommended for further experimental research as possible (AChE) inhibitors.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations.

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations.

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

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

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors.

FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP. Based on the docking results, we identified structure-based pharmacophore characteristics resistant to potent FLT3 inhibitors. The best hypothesis was corroborated using test and decoy sets, and the verified hypo was utilized to screen the chemical database. The hits from the pharmacophore-based screening were then screened again using a structure-based method that included molecular docking at various precisions; the selected molecules were further examined and refined using drug-like filters and ADMET analysis. Finally, two hits were picked out for molecular dynamic simulation. The results showed two hits were expected to have potent inhibitory activity and excellent ADMET characteristics, and they might be used as new leads in the development of FLT3 inhibitors.Communicated by Ramaswamy H. Sarma.

Combined computational approaches for developing new anti-Alzheimer drug candidates: 3D-QSAR, molecular docking and molecular dynamics studies of liquiritigenin derivatives.

Butyrylcholinesterase is an acetylcholine-degrading enzyme involved in the memorization process, which is becoming an interesting target for the symptomatic treatment of Alzheimer's disease. In the present investigation, the structure-activity relationship of a set of Liquiritigenin derivatives recently revealed to be Butyrylcholinesterase inhibitors was studied basing on comparative field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMISA). As a result, performant models with high predictive capability have been developed (CoMFA model: R2 = 0.91, Q2 = 0.62, R2 pred = 0.85; CoMISA model: R2 = 0.92, Q2 = 0.59, R2 pred = 0.83) and implemented to design new Liquiritigenin derivatives with improved activity. Besides, the affinity of the designed derivatives towards the active site of Butyrylcholinesterase, was confirmed by molecular docking and molecular dynamics studies. Moreover, they exhibited good pharmacokinetics properties. Accordingly, the outcomes of the present investigations can provide important direction for the development of new anti-Alzheimer's drug candidates.

Unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitors: Molecular docking, molecular dynamics, and ADME scoring investigations.

COVID-19 pandemic caused by very severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) agent is an ongoing major global health concern. The disease has caused more than 452 million affected cases and more than 6 million death worldwide. Hence, there is an urgency to search for possible medications and drug treatments. There are no approved drugs available to treat COVID-19 yet, although several vaccine candidates are already available and some of them are listed for emergency use by the world health organization (WHO). Identifying a potential drug candidate may make a significant contribution to control the expansion of COVID-19. The in vitro biological activity of asymmetric disulfides against coronavirus through the inhibition of SARS-CoV-2 main protease (Mpro) protein was reported. Due to the lack of convincing evidence those asymmetric disulfides have favorable pharmacological properties for the clinical treatment of Coronavirus, in silico evaluation should be performed to assess the potential of these compounds to inhibit the SARS-CoV-2 Mpro. In this context, we report herein the molecular docking for a series of 40 unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitor. The optimal binding features of disulfides within the binding pocket of SARS-CoV-2 endoribonuclease protein (Protein Data Bank [PDB]: 6LU7) was described. Studied compounds were ranked for potential effectiveness, and those have shown high molecular docking scores were proposed as novel drug candidates against SARS-CoV-2. Moreover, the outcomes of drug similarity and ADME (Absorption, Distribution, Metabolism, and Excretion) analyses have may have the effectiveness of acting as medicines, and would be of interest as promising starting point for designing compounds against SARS-CoV-2. Finally, the stability of these three compounds in the complex with Mpro was validated through molecular dynamics (MD) simulation, in which they displayed stable trajectory and molecular properties with a consistent interaction profile.

Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as New HIV1 Inhibitors.

The present study deals with the in silico of 45 indolyl-aryl-sulfones known as anti-HIV1. The data were collected from recent previously reported inhibitors and divided into a sub-set of 33 compounds as the training set and the remaining 12 compounds were kept in the test set. The selected pharmacophore-ADRRR-yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.930, Q2 = 0.848, and RMSE = 0.460). The predictive power of the established pharmacophore model was validated with an external test (r2 = 0.848). A systematic virtual screening workflow shows an enrichment factor and has revealed a high predictive power. Then the model was used to screen the filtered PubChem database mapping all chemical features of model pharmacophore. The recognized hits were further assessed by in silico ADMET studies. Molecular dynamics also used to explore the stability of obtained complexes. Finally, these selected compounds are probably to become a good lead molecule for the development of effective anti-HIV-1 drugs.

In silico investigation of phytoconstituents from Cameroonian medicinal plants towards COVID-19 treatment.

In silico studies performed on the metabolites of four Cameroonian medicinal plants with a view to propose potential molecules to fight against COVID-19 were carried out. At first, molecular docking was performed for a set of 84 selected phytochemicals with SARS-CoV-2 main protease (PDB ID: 6lu7) protein. It was further followed by assessing the pharmacokinetics and pharmacological abilities of 15 compounds, which showed low binding energy values. As the screening criteria for their ADMET properties were performed, only two compounds have shown suitable pharmacological properties for human administration which were shortlisted. Furthermore, the stability of binding of these compounds was assessed by performing molecular dynamics (MD) simulations. Based on further analysis through molecular dynamics simulations and reactivity studies, it was concluded that only the Pycnanthuquinone C (17) and the Pycnanthuquinone A (18) extracted from the Pycnanthus angolensis could be considered as candidate inhibitors for targeted protein. Indeed, we expect that these compounds could show excellent in vitro and in vivo activity against SARS-CoV-2. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01939-7.

Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies.

In the present study, a quantitative relationship between the biological inhibitory activity of alpha-amylase and molecular structures of novel benzimidazole derivatives is analyzed in silico. The best QSAR model screened via MLR technique indicated that the exact mass, topological diameter and numerical rotational bonding structural properties of benzimidazole derivatives highly affect the bioactivity of these compounds against α-amylase. Based on the structural properties identified via linear QSAR model favorable for improving pIC50 of benzimidazole derivatives, fourteen new molecules bearing benzimidazole radicals were designed and their biological inhibitory activity against α-amylase was improved. QSAR model predictions showed that the designed molecules exhibited a higher potential biological level activity IC50 than acarbose used in positive control (IC50= 1.46 µM). Screening of drug-like properties, pharmacokinetics and toxicity of the proposed molecules led to select three molecules as candidates for use as drug aid to ingest starch and glycogen. As a result, using molecular docking simulations, the docking poses of the three molecules inside the α-amylase receptor pocket (PDB code: 1HNY) were predicted. Also, the most important potential interactions between the active amino acid sites in α-amylase protein pocket and the proposed drug molecules were described. The obtained hypotheses regarding the stability of the proposed molecules inside α-amylase pocket were validated by carrying out molecular dynamic simulations in aqueous background similar to the ones of proteins. The DM results confirmed the optimal stability of the α-amylase backbone with the drug molecules proposed in this computational investigation.

In Vitro and In Silico Determination of Some N-ferrocenylmethylaniline Derivatives as Anti-Proliferative Agents Against MCF-7 Human Breast Cancer Cell Lines.

BACKGROUND: Since the binding of estradiol to its receptor promotes breast cancer cell proliferation (in the ER+ tumours), many molecules targeting this protein have been synthesized to counteract the estradiol action. Ferrocene derivatives have proved their efficiency against hormone-dependent breast cancer cells (MCF-7). OBJECTIVE: In this study, we aimed to find new ferrocene derivatives having pharmacochemistry properties as potential drugs against human breast cancer cells. METHODS: A series of 29 N-ferrocenylmethylaniline derivatives A0-A28 were synthesised, and their anti-proliferative activity against both hormone-dependent (MCF-7) and independent (MDA-MB 231) human breast cancer cell lines were performed using the MTT test. Molecular docking and drug-likeness prediction were also performed for the five most active derivatives towards MCF-7. A QSAR model was also developed for the perdition of the anti-proliferative activity against MCF-7 cell lines using molecular descriptors and MLR analysis. RESULTS: All studied derivatives demonstrated better cytotoxicity against MCF-7 compared to the MDA-MB-231 cell lines, and compounds A2, A9, A14, A17 and A27 were the most potent ones but still less active than the standard anticancer drug, crizotinib. The QSAR study revealed good predictive ability, as shown by R2 cv = 0.848. CONCLUSION: In vitro and in silico results indicated that derivatives A2, A9, A14, A17, and A27 possess the highest anti-proliferative activity; these results can be used to design more potent N-ferrocenylmethylaniline derivatives as anti-proliferative agents.

Combined molecular docking and dynamics simulations studies of natural compounds as potent inhibitors against SARS-CoV-2 main protease.

Main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2 the new strain of coronavirus. In this study, we evaluated biologically active compounds present in medicinal plants as potential SARS-CoV-2 Mpro inhibitors, using a molecular docking study with Autodock Vina software. Top seven compounds Afzelin, Phloroglucinol, Myricetin-3-O- rutinosid Tricin 7-neohesperidoside, Silybin, Kaempferol and Silychristin among 50 molecules of natural Origin (Algerian Medicinal plants) were selected which had better and significantly low binding energy as compared to the reference molecule with binding affinities of -9.3, -9.3, -9, -8.9, -8.5, 8.3 and -8.3 kcal mol-1 respectively. Then, we analyzed the ADME properties of the best 7 ligands using the Web server SwissADME. Two of small molecules have been shown to be the ideal candidates for further drug development. Finally, the stability of the both compounds complexed with Mpro was validated through molecular dynamics (MD) simulation, they displayed stable trajectory (RMSD, RMSF) and molecular properties with consistent interaction profile in molecular dynamics simulations, moreover, Silybin could form more stable complex with Mpro than Silychristin.Communicated by Ramaswamy H. Sarma.

Multi-combined 3D-QSAR, docking molecular and ADMET prediction of 5-azaindazole derivatives as LRRK2 tyrosine kinase inhibitors.

The enzyme Leucine-rich repeat kinase 2 (LRRK2) has become a target of therapeutic interest in Parkinson research.Athree-dimensional quantitative structure-activity relationships(3D-QSAR) study was performed on twenty six azaindazole derivatives as LRRK2 inhibitorsobtained using rigid body alignment (Distill). CoMFA and CoMSIA model shave achieved high activity-descriptor relationship efficiency of 96% and 93% as shown by the regression-coefficient (R2=0.961 and 0.933) and were found statistically significant with cross validated coefficient (Q2CV= 0.625 and 0.554), respectively.3D-QSAR models were externally validated by a test set of sixbioactive compounds showing satisfactory predicted correlation coefficient (R2pred) of 0.865 and 0.853 for CoMFA and CoMSIA models, respectively. Besides, Y-randomization test was also performed to ensure the robustness of the obtained3D-QSAR models. This study provides valuable clues to design new compounds against LRRK2. Docking studies suggested that the ligand (new designed compound C2) has more potential than the ligand of reference 4K4 and confirm the obtained results from 3D-QSAR studies. Furthermore, the newly designed compounds and ligand of reference 4K4 were analyzed for their ADMET properties and drug likeness. These results would be of great help in leading optimization for developing new anti-Parkinson drug. Communicated by Ramaswamy H. Sarma.

Cameroonian medicinal plants as potential candidates of SARS-CoV-2 inhibitors.

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic instigated by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which changed the daily train of the world's population and cause several dead. Despite the significant efforts made in developing vaccines and therapeutic drugs, there is currently no available effective treatment against this new coronavirus infection, hence the need to continue research which is aimed at limiting the progression of this virus. The present study which has as objective to carry out in silico studies on the metabolites of some Cameroonian medicinal plants of the Asteraceae family with a view to propose potential molecules to fight against COVID-19. The selected plants are commonly used to treat respiratory infectious diseases, and for this reason they may contain some constituents which could exhibit an antiviral activity against SARS-CoV-2. In this work, a set of 74 naturally occurring compounds are computed with SARS-CoV-2 main protease protein (PDB ID: 6lu7) and spike protein (PDB ID: 6m0j) for their affinity and stability using binding energy analysis and molecular docking. Chrysoeriol-7-O-ß-D-glucuronopyranoside (compound 16) has showed promising results including excellent Absorption, Distribution, Metabolism and Excretion (ADME) parameters as well as insignificant toxicity. Finally, the stability of this compound is complex with the two proteins validated through molecular dynamics (MD) simulation, they displayed stable trajectory and molecular properties with consistent interaction profile in molecular dynamics simulations. These findings call for further in vitro and in vivo challenges of phytoconstituents against the COVID-19 as a potential agent to fight the spread of this dramatic pandemic.Communicated by Ramaswamy H. Sarma.

QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823.

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) statistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

Combined 3D-{QSAR} and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors.

The discovery of antibacterials is considered one of the greatest medical achievements of all time. In this work, a combination of three computational analyzes: 3D-QSAR, molecular docking and ADME evaluation were applied in thienopyrimidine derivatives intended toward gram-positive bacterium Staphylococcus aureus. The validity of 3D-QSAR model was tested with a set of data which is divided into a training and a test set. The two models constructed (CoMFA and CoMSIA) show good statistical reliability (q2 = 0.758; r2 = 0.96; r2pred = 0.783) and (q2 = 0.744; r2 = 0.97; r2pred = 0.625) respectively. In addition, docking methods were applied to understand the structural features responsible for the affinity of the ligands in the binding of S. aureus DNA gyrase. Drug likeness and ADME analysis applied in this series of new proposed compounds, have shown that the five lead molecules would have the potential to be effective drugs and could be used as a starting point for designing compounds against Staphylococcus aureus.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 â€‹+ â€‹G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R2, R2 test and Q2cv (R2 â€‹= â€‹0.838, R2 test â€‹= â€‹0.735, Q2 cv â€‹= â€‹0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

Combined docking methods and molecular dynamics to identify effective antiviral 2, 5-diaminobenzophenonederivatives against SARS-CoV-2.

The aim of this work is to contribute to the research in finding lead compounds for clinical use, to identify new drugs that target the SARS-CoV-2 virus main protease (Mpro). In this study, we used molecular docking strategies to analyze 2.5-diaminobenzophenone compounds against Malaria and to compare results with the Nelfinavir as a FDA-approved HIV-1 protease inhibitor recommended for the treatment of COVID-19. These efforts identified the potential compounds against SAR-COV-2 Mpro with the docking scores ranges from -6.1 to -7.75 kcal/mol, which exhibited better interactions than the Nelfinavir. Among thirty-six studied, compounds 20c, 24c, 30c, 34c, 35c and 36c showed the highest affinity and involved in forming hydrophobic interactions with Glu166, Thr24, Thr25, and Thr26 residues and forming H-bonding interactions with Gln189, Cys145, and His41residues. Pharmacokinetic properties and toxicity (ADMET) were also determined for identified compounds. This study result in the identification of two compounds 35 and 36 having high binding affinity, good pharmacokinetics properties and lowest toxicity. The structural stability and dynamics of lead compounds within the active site of 3CLpro was also examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the two complexes remain stable during the entire duration of simulation. We have shown that these two lead molecules would have the potential to act as promising drug-candidates and would be of interest as starting point for designing compounds against the SARS-CoV-2.

Discovery of Potent SARS-CoV-2 Inhibitors from Approved Antiviral Drugs via Docking and Virtual Screening.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten patients, societies and healthcare systems around the world. There is an urgent need to search for possible medications. OBJECTIVE: This article intends to use virtual screening and molecular docking methods to find potential inhibitors from existing drugs that can respond to COVID-19. METHODS: To take part in the current research investigation and to define a potential target drug that may protect the world from the pandemic of corona disease, a virtual screening study of 129 approved drugs was carried out which showed that their metabolic characteristics, dosages used, potential efficacy and side effects are clear as they have been approved for treating existing infections. Especially 12 drugs against chronic hepatitis B virus, 37 against chronic hepatitis C virus, 37 against human immunodeficiency virus, 14 anti-herpesvirus, 11 anti-influenza, and 18 other drugs currently on the market were considered for this study. These drugs were then evaluated using virtual screening and molecular docking studies on the active site of the (SARS-CoV-2) main protease (6lu7). Once the efficacy of the drug is determined, it can be approved for its in vitro and in vivo activity against the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which can be beneficial for the rapid clinical treatment of patients. These drugs were considered potentially effective against SARS-CoV-2 and those with high molecular docking scores were proposed as novel candidates for repurposing. The N3 inhibitor cocrystallized with protease (6lu7) and the anti-HIV protease inhibitor Lopinavir were used as standards for comparison. RESULTS: The results suggest the effectiveness of Beclabuvir, Nilotinib, Tirilazad, Trametinib and Glecaprevir as potent drugs against SARS-CoV-2 since they tightly bind to its main protease. CONCLUSION: These promising drugs can inhibit the replication of the virus; hence, the repurposing of these compounds is suggested for the treatment of COVID-19. No toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA. However, the assessment of these potential inhibitors as clinical drugs requires further in vivo tests of these drugs.

Vibronic coupling to simulate the phosphorescence spectra of Ir(III)-based OLED systems: TD-DFT results meet experimental data.

The electronic and optical properties of six iridium imidazolylidene complexes (1a, 1b, 2, 2b, 3, 3b) that are strong candidates for use in OLED systems were investigated theoretically. Computations using DFT and TD-DFT methods were performed to explain the observed optical properties of these complexes. Observed absorption bands were assigned and the lowest triplet excited states were computed. Whereas complexes 1a and 1b are nonemissive in solution, the simulated phosphorescence spectra of complexes 2, 2b, 3, and 3b were in good agreement with the observed spectra when the vibrational contributions to the electronic transitions were taken into account. The use of vibronic coupling allowed us to reproduce and explain the structured phosphorescence spectra of complexes 2 and 2b, as well as the absence of such structure from the spectra of complexes 3 and 3b. Graphical Abstract Successful simulation of the phosphorescence spectra of Ir(III)-based OLED xsystems.