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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 theThe online version contains supplementary material available at 10.1007/s11224-022-01939-7.

In the light of the serious diseases attributed to it, there is an urgent and inescapable need to hunt for antiviral medications for Coxsackievirus B3 (CVB3).

Leishmaniasis is a severe disease caused by protozoan parasites of the genus Leishmania and it is accountable for sizable morbidity and mortality worldwide.

Global energy consumption has increased due to population growth and economic development. Solar energy is one of the most important renewable energy sources for human consumption. In this research, four novel organic dyes (D2-D5) of the D-A-π-A structure based on triphenylamine (TPA) were studied theoretically using DFT and TD-DFT techniques for future usage as dye-sensitized solar cells (DSSCs). The effects of modifying the π-spacer of the reference molecule D1 on the structural, electronic, photovoltaic, and optical characteristics of the D2-D5 dyes were studied in detail. D2-D5 exhibited band gaps (

A study was carried out on the 1.3 dipolar cycloaddition reaction between Phenylethyne and Benzyl (diazyn-1-ium-1-yl) azanide, using the different theoretical approaches, to know precisely the reaction path, and the regioselectivity of reaction, as well as the transition state. We carried out this study by using the DFT method on the one hand, on the other hand we studied the antibacterial activity of two isomers 1.4 and 1.5 obtained from cycloaddition reaction on two therapeutic targets E. coli and Helicobacter pylori, using Molecular docking.

Plasmepsin II is a key enzyme in the life cycle of the Plasmodium falciparum parasite responsible for malaria, a disease that is causing deaths on a worldwide scale. Recently, plasmepsin II enzyme has gained much importance as an attractive drug target for the investigation of antimalarial drugs. In this sense, structure-based virtual screening have been utilized as tools in the process of discovering novel natural compounds based on quinoline as potential plasmepsin II inhibitors. Among the 58 quinoline derivatives isolated from different plants was screened by utilizing docking molecular, ADMET approaches, molecular dynamics simulation and MM-PBSA binding free energy. The first step in this work is building the 3 D structures of the plasmepsin II enzyme by using the SWISS-MODEL software. The optimized structures were subjected to virtual screening by Autodock Vina, an entity implicated in PyRx software. 21 were selected based on their binding affinity. The binding modes and interactions of the top-21 selected compounds were evaluated using AutoDock 4.2. Then, the pharmacokinetic proprieties and toxicity of these compounds were evaluated using ADMET analysis. Ten compounds were predicted to have ADMET characteristics with no side effects. Compounds M49 and M53 were found to be potential inhibitors. The stability of the selected two compounds was confirmed by MD simulation and MM/PBSA calculation during 200 ns. This study can be used to predict and to design new antimalarial drugs. Communicated by Ramaswamy H. Sarma

In this study, Crocin, Digitoxigenin, Beta-Eudesmol, and Favipiravir were docked in the active site of SARS-CoV-2 main protease (PDB code: 6LU7). The docking study was followed by Molecular Dynamics simulation. The result indicates that Crocin and Digitoxigenin are the structures with the best affinity in the studied enzyme's binding site. Still, Molecular Dynamics simulation showed that Digitoxigenin is the molecule that fits better in the active site of the main protease. Therefore, this molecule could have a more potent antiviral treatment of COVID-19 than the other three studied compounds. © 2021 by the authors.

The purpose of the paper is to discuss the various methods and computational approaches, which are used in computer-aided drug design. For this reason, pyrimidine and azaindole derivatives have been used to study the inhibitory activity of CaMKKII. It is an enzyme that enters the brain to greatly reduce food from regulating the production of Ghrelin that is synthesized by the stomach and acts on the hypothalamus. The obtained results from different techniques such as the 3D-QSAR, molecular docking, and ADMET were applied to study series of new CaMKKII inhibitors of 23 molecules based on pyrimidine and azaindole derivatives. The CoMFA and CoMSIA models were used in 19 molecules in the training set that give high values of determination coefficient R2 0.970 and 0.902 respectively, and significant values of Leave-One-Out cross-validation coefficient Q2 0.614 and 0.583 respectively. The predictive capacity of this model was examined by external validation though using a test set of four compounds with a predicted determination coefficient test R2ext of 0.778 and 0.972 successively. The method of alignment adapted with the appropriate parameters gave credible models. The CoMFA and CoMSIA models produce the contour maps which were used to define a 3D-QSAR mode. DOI: http://dx.doi.org/10.17807/orbital.v13i4.1608

This research aims to screen out the effective bioactive compounds from Coriander (

These are supplementary files oftrajectories of DENV virus inhibitory activity from molecular dynamics simulation to the preprint/paper "HQSAR, CoMFA, CoMSIA docking studies and simulation MD on quinazolines/quinolines derivatives for DENV virus inhibitory activity"using Maestro (Schrödinger, LLC, New York, NY, 2018) (https://doi.org/10.21203/rs.3.rs-1465758/v1). The files include: Raw Desmond trajectory files of the MD simulations of the compounds N21 and N25 [out.cms and the full trj files].

Fluconazole and Voriconazole are individual antifungal inhibitors broadly adopted for treating fungal infections, including Candida Albicans. Unfortunately, these medicines clinically used have significant side effects. Consequently, the improvement of safer and better therapy became more indispensable. In this study, a set of 27 1,2,4-triazole compounds have been tested as potential Candida Albicans inhibitors by using different theoretical methods. The created comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) contour maps significantly impacted the development of novel Candida Albicans inhibitors with valuable activities. The mode of interactions between the 1,2,4-triazole inhibitors and the targeted receptor was studied by molecular docking simulation. The proposed new molecule P1 showed satisfied stability in the active pocket of the targeted receptor compared to the more active molecule in the dataset compared to Fluconazole medication. Meanwhile, the binding energy obtained by molecular docking for molecule P1 is - 9.3 kcal/mol compared with - 6.7 kcal/mol for Fluconazole medication. Also, MM/GBSA value obtained by molecular dynamics simulations at 100 ns for molecule P1 is - 33.34 kcal/mol compared with - 15.85 kcal/mol for Fluconazole medication. In addition, molecule P1 showed good oral bioavailability and was non-toxic according to ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Therefore, the results indicated compound P1 might be a future inhibitor of Candida Albicans infection.

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) (Q2 = 0.604, R2 = 0.863, rext 2 = 0.701) and comparative molecular similarity indices analysis (CoMSIA) (Q2 = 0.606, R2 = 0.854, rext 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.

In this study, quantitative mathematical models were established to understand the relationship between a series of 26 quinazoline / quinoline derivative and their biological activity against DENV virus using 3D-QSAR and HQSAR analysis. According to the results we obtained, the models have good predictability:the HQSAR model (Q 2 = 0.82, R 2 = 0.95), the CoMFA model with Q 2 = 0.755, R 2 = 0.94 and CoMSIA with Q 2 = 0.76, R 2 = 0.930. It should be noted that all three models successfully meet all external validation criteria. Molecular docking and dynamic simulation have been employed to explain the mode of binding between the ligand and the active site of the protein, and assess, justify ligand stability in the active receptor site respectively. The results of this research provide an information base for discovering new inhibitors of DENV virus.

In search of new high-performance materials to be used in organic solar cells by changing the bridge of molecules already synthesized, we used the functional density theory (DFT) and Time-dependent density-functional theory (TD/DFT) with several methods such as B3LYP, PBEPBE, B3PW91, mPW1PW91 and the basic set 6-31G (d, p), the electronic structures and optoelectronic properties of four organic dyes based on 2-(6-substituted-anthracen-2-yl)-thiophene as the π conjugated bridge, different amines as electron donors, and cyanoacrylic acid group as an electron acceptor have been calculated and discussed theoretically in the aim to obtain the most suitable method. The calculated electronic levels HOMO, LUMO, and Egap of the studied compounds show that the B3LYP method with the 6-31G base set (d, p) offers better performances such as lower energy gap. Then, we studied the influence of the change of bridge (anthracene) by thiophene and benzene on the electronic, optical and photovoltaic properties of these organic dyes.TD-B3LYP functional was used to describe optoelectronic properties such as the UV–visible spectrum for the various compounds studied. Furthermore, the free energy of electron injection (ΔG inject), LHE and O.S were calculated and analyzed. In conclusion, the calculated results reveal that the new compounds studied can be used as good candidates for dye-sensitized solar cells (DSSC) due to its best electronic and optical properties and good photovoltaic parameters except for compound M3..

A new class of benzimidazoles bearing bis-Schiff bases as α-glucosidase inhibitory was studied based on the combination of two computational techniques such as 3D-QSAR and molecular docking. The CoMFA and CoMSIA QSAR models were developed from fifteen compounds in the training set and four compounds in the test set giving Q2 values of 0.587 and 0.597 respectively, and R2 values of 0.970 and 0.990 respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The CoMFA and CoMSIA contour maps allowed the authors to recognize regions where the activity can be increased or decreased by suitable substitutions. According to these contour maps they have proposed three new compounds with high predicted activities. Moreover, to confirm the stability of these newly designed molecules in the receptor with PDB: 3A4A, a Surflex-docking was applied.

Acarbose and voglibose are α -glucosidase inhibitors that have been used for controlling of diabetes mellitus. Unfortunately, these drugs have many side effects. Consequently, the discovery of new agents with high α -glucosidase inhibitory activity and weak side effects becomes of great importance. To that end, a set of 23 benzothiazole compounds have been studied using 3D-QSAR modeling and molecular docking simulation in order to predict new molecules with important α -glucosidase inhibitory activity. CoMFA and CoMSIA models using eighteen compounds in the training set give significant Q2 values of 0.553 and 0.75, and important R2 values of 0.93 and 0.942, respectively. The five remaining molecules wereemployed in order to test the proficiency of 3D-QSAR models and the predicted determination coefficients R2test values are 0.74 and 0.87 for CoMFA and CoMSIA models, respectively. CoMFA and CoMSIA contour maps were afforded much helpful information to determine the preferred and unpreferred regions impacting the activity; as a result, we propose new benzothiazole compounds with important predicted activities. Meanwhile, to guess the interaction between a newly designed compounds and the most active molecule (compound 8) with α-glucosidase receptor (PDB ID: 3L4T), a molecular docking was conducted. The docking outcomes elucidated that the predicted compound X1 was stable in the active pocket of α-glucosidase receptor. Finally, the newly benzothiazole compounds have been assessed for their oral bioavailability and toxicity using ADMET properties and drug likeness. These findings would be of great aid in leading optimization for new drug discovery that can resolve the biggest challenge related of diabetes mellitus.

The World Health Organization (WHO) considers cancer as the deadliest disease, due to the increase in death in the 21 century. In the search for new therapeutic molecules, the scientific researches consider the proviral integration moloney (Pim) kinases as promising therapeutic targets for the treatment of hematological cancers. A series of thirty-four 3,5-disubstituted indole derivatives as potent Pim1 kinase inhibitors were studied using 3D-QSAR (CoMFA and CoMSIA) and molecular docking. CoMFA analysis showed Q2 value of 0.56, R2 value of 0.86 and rtest2 value of 0.78, while CoMSIA analysis showed a Q2 value of 0.73, R2 value of 0.93 and rtest2 value of 0.80. The models were generated using 28 compounds as training set and 6 compounds as test set. Furthermore, the contour maps acquired from the CoMSIA and CoMFA models were used to rationalize the principal structural requirement responsible for the activity. As results, four new compounds were designed in silico. Furthermore, the newly designed compound X1 and the most active compound 28 were subjected to molecular docking study in order to validate their stability. Molecule docking shows that compound X1 has greater stability than compound 28. The newly compounds were evaluated for in silico toxicity properties and verified the five Lipinski rules for wet-lab applicability.

A forty-two compounds series of potential epidermal growth factor receptor kinase inhibitors of Pyrazole derivatives containing thiourea analogs have been subjected to 3D-QSAR (3-Dimensional Quantitative Structural-Activity Relationship) studies using CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis). The training set and the test set of pyrazole derivatives have been used for the generation and validation of QSAR model, respectively. Dataset alignment has been performed using the lowest energy conformer of the most active compound. The best-generated CoMFA and CoMSIA models exhibit conventional determination coefficients R2 of 0.982 and 0.980 as well as the Leave One Out cross validation determination coefficients Q2 of 0.734 and 0.801, respectively. Moreover, the predictive ability of those models was evaluated by the external validation using a test set of five compounds with predicted determination coefficients R2test of 0.932 and 0.846, respectively. Based on those satisfactory results, ten new compounds have been designed and predicted by in silico Drug likeness and ADMET method. In addition, molecular docking studies were carried out to explore the binding interactions of the selected compounds with the enzyme. This study could expand the understanding of pyrazole derivatives as anti-cancer agent and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity.

In search of novel and more potent p-aminobenzoic acid derivatives previously evaluated as effective acetylcholinesterase inhibitors for the control of Alzheimer’s disease (AD), an integrated computational approach of three-dimensional quantitative structure–activity relationship and molecular docking were performed on a series of 20 compounds. The 3D-QSAR approach was applied to statistically study the structure-activity relationships (SAR) and had yielded good statistical significance for two high predictive models; comparative molecular field analysis (CoMFA: Q2=0.785; R2=0.936; rext2= 0.818) and comparative molecular similarity indices analysis (CoMSIA: Q2=0.831; R2=0.944; rext2= 0.931). Detailed analysis of the predictive models contour maps revealed that the hydrophobic and electrostatic fields govern the bioactivity and provided much helpful information to understand the features requirement in order to develop new potent acetylcholinesterase inhibitors. These findings were very useful for designing four novel inhibitors with enhanced activities targeting acetylcholinesterase. Through molecular docking, the newly designed compounds and compound 19 were docked on AChE as the protein target which helped to analyze the interaction characteristics and explore the binding modes at the active sites of the AChE. This work may be of utility for guiding the rational design of a new generation of acetylcholinesterase inhibitors. DOI: http://dx.doi.org/10.17807/orbital.v12i4.1467

The main objective of this study is to develop 2D- and 3D-QSAR statistical models for a series consisting of 28 molecules. The authors started with a 2D study based on principal component analysis (PCA), multiple linear regression (RLM), and nonlinear regression (RNLM). The models were developed using 28 molecules with a pIC50 between 5.70 and 6.70. Then they applied 3D-QSAR analysis based on the partial least squares (PLS) method. For 3D-QSAR, they used the molecular field comparative analysis (CoMFA) and comparative molecular similarity index (CoMSIA) methods. For this analysis, they have worked on a training set of 24 compounds, which then give acceptable and reliable values of Q2 (0.791 and 0.538, respectively) and R2 (0.974 and 0.98, respectively). To determine the quantitative 3D-QSAR, the interpretations were based on the contour maps which are produced by the CoMFA and CoMSIA models. In addition, molecular docking is also one of the most important methods for confirming the binding interactions of predicted molecules with their receptors.

The α-amylase is an enzyme of a highly conserved glycoside hydrolase family, α-amylase inhibitors can be used as clinical agents for the treatment of Diabetes Mellitus (DM). A 3D-QSAR study was performed on 45 2-aryl benzimidazole derivatives, which have been identified as insulin-independent antidiabetic agents. The 3D-QSAR technique includes CoMFA with Q2 of 0.696 and R2 of 0.860 and CoMSIA with Q2 of 0.514 and R2 of 0.852. Both models were derived from a training set of 37 compounds based on an appropriate method of alignment, while the predictive ability was approved by a test set containing 8 compounds with rext2 values of 0.990 and 0.987, respectively. Moreover, contour maps generated from CoMFA and CoMSIA models provided much helpful information to figure out the features requirements that have control over the activity. To further reinforce the 3D-QSAR results, the molecular docking method was implemented which led to design new potent insulin-independent antidiabetic compounds with high predicted activity values.

The identification of chemotherapeutic drugs against Novel Coronavirus (2019-nCoV) is a significant requirement due to the rapid rise in deaths due to Corona Viral Infection all around the world. Therefore, it is of interest to document the molecular docking analysis data of 32 N-substituted Oseltamivir derivatives inhibitors of influenza virus H5N1 with the Novel Coronavirus main protease (2019-nCoV). We describe the optimal binding features of Oseltamivir derivatives with the SARS-Cov-2 main protease (Code PDB: 6LU7) for further consideration.

SARS-COV-2 virus causes (COVID-19) disease; it has become a global pandemic since 2019 and has negatively affected all aspects of human life. Scientists have made great efforts to find a reliable cure, vaccine, or treatment for this emerging disease. Efforts have been directed towards using medicinal plants as alternative medicines, as the active chemical compounds in them have been discovered as potential antiviral or anti-inflammatory agents. In this research, the potential of Saussurea costus (S. Costus) or QUST Al Hindi chemical consistent as potential antiviral agents was investigated by using computational methods such as Reverse Docking, ADMET, and Molecular Dynamics with different proteases COVID-19 such as PDB: 2GZ9; 6LU7; 7AOL, 6Y2E, 6Y84. The results of Reverse Docking the complex between 6LU7 proteases and Cynaropicrin compound being the best complex, as the same result, is achieved by molecular dynamics. Also, the toxicity testing result from ADMET method proved that the complex is the least toxic and the safest possible drug. In addition, 6LU7-Cynaropicrin complex obeyed Lipinski rule; it formed ≤5 H-bond donors and ≤10 H bond acceptors, MW 500 Daltons, and octanol/water partition coefficient5.

The p53 is a tumor suppressor protein that adjusts cell cycle and growth arrest as well as genes that restore DNA damage and apoptosis. Murine double minute 2 (MDM2) is a main p53 antagonist. We created a novel QSAR model using a series of highly active spiro [pyrrolidin-3,2-oxindoles] that consisted of 29 compounds that were experimentally validated to inhibit the MDM2-p53 interaction. Three optimal models have been developed CoMFA/E+S, CoMSIA/S+H+A and HQSAR have revealed good statistical results, but the CoMSIA mode only which validates all the external validation tests applied successfully. Based on the CoMSIA/S+H+A model was carefully chosen to design four compounds with values ​​of inhibitory activity greater than the highly active compound in the data set. The Newly designed compounds were docked in the target receptor binding site (ID: 4LWU). The newly designed compound Pred 01 showed the highest affinity with a value of -9.4kcal / mol, while compound N°04 which represents the data set and control compound (Nutlin-3) showed binding energies of the order of -8.8 kcal / mol and -8.2kcal / mol, respectively. In addition, the roles of lipinski and veber were estimated, the results obtained demonstrate that the proposed molecules involve good oral bioavailability and an ability to diffuse through different biological barriers. For in-depth study, The Pred01 / receptor, N°04 / receptor and Nutlin-3 / receptor complexes were selected via dynamic simulation analyzes with a simulation time of 100 ns and, also their free binding energy was examined operating the MM-GBSA approach. The molecular docking results obtained accentuate the crucial residues responsible for the ligand / protein interaction, providing insight into the mode of interaction. The MD simulation analysis confirms the conformational stability of the selected complexes during the MD trajectory, and the fluctuations recorded are insignificant. The results of MM-GBSA reveal that the new compound Pred 01 exhibits the lowest free energy, which confirms the result of molecular docking.

This research aims to screen out the effective bioactive compounds from Coriander (Coriandrum sativum L.), which may be novel potential inhibitors of Eubacterium rectale for the prevention of colorectal cancer (CRC). A series of 8 coriander-derived chemical compounds previously assessed for their anti-inflammatory, antioxidant, and antidiabetic activities were tested against Carbohydrate ABC transporter substrate-binding protein and compared to the standard inhibitor Acarbose, to support their use as novel Eubacterium rectale inhibitors. Herein, these derivatives were submitted to a thorough analysis of docking studies, in which detailed interactions of the selected phytocompounds with carbohydrate ABC transporter substrate-binding protein were revealed. Molecular docking analysis recommends Rutin, Gallocatechin, and Epigallocatechin as the most potential Eubacterium rectale inhibitors among the eight selected phytochemical compounds. Subsequently, the stability of the three selected phytochemical complexes was checked using molecular dynamics (MD) simulation at 100 ns and Molecular Mechanics combined with Poisson-Boltzmann Surface Area (MM-PBSA). The results show quite good stability for Rutin and Gallocatechin. In silico ADMET prediction was performed on the selected compounds, and the findings revealed a reasonably good ADMET profile for both Rutin and Gallocatechin. The current findings predict that Gallocatechin could be a better CRC preventive natural compound, and, further in vitro, in vivo and clinical studies may confirm its therapeutic potential. Communicated by Ramaswamy H. Sarma

Cancer is one of the greatest challenges that worry the minds of scientists and threatens human life. Despite the presence of several drugs on the market, their effectiveness remains limited by its resistance. In this research, the Monte Carlo approach was used for QSAR modelling applying the representation of the molecular structure by the SMILES and optimal molecular descriptors. Correlation Ideality (IIC) and Correlation Contradiction Index (CCI)) were introduced as validation parameters to further estimate the predictive ability of the developed models. The statistical quality of the model developed with (IIC) was good compared to those without (IIC). The best QSAR model of the following statistical parameters: (R²train= 0.816, R²valid = 0.825) was selected to generate the activity-increasing and decreasing promoters studied, and these are the basis for the design of seven new candidates as an antiproliferative inhibitory agent. Additionally, the newly designed molecules, the most active compound in the dataset, erlotinib and melphalan as control compounds were docked in the EGFR receptor binding site. The docking results discovered that the proposed candidates had the highest potential and energy affinity. Besides, Lipinski’s Rule of Five, Synthetic Accessibility and ADME/Tox were performed to assess bioavailability and drug-likeness of proposed compounds. In addition, MD simulation accompanied by MM-PBSA analysis discovered that compound A1 and the screened compounds were stable and did not show significant fluctuations throughout the simulation time. Generally, this research showed that the selected model well explains the antiproliferative activity and also that the proposed compounds have high activity, good binding affinity and stable conformation with the reported target protein.

Tubulin plays an indispensable role in regulating various important cellular processes. Recently, it is known as a hopeful therapeutic target for the rapid division of cancer cells. Novel series of 2-oxoquinoline arylaminothiazole derivatives have been recently identified as promising tubulin inhibitors with potent cytotoxicity activity against HeLa cancer cell line. In this study, a 3D-QSAR approach by using CoMFA and CoMSIA techniques was applied to the reported derivatives to understand their pharmacological essentiality contributing to the tubulin inhibition activity and selectivity. The optimum CoMFA and CoMSIA models were found to have significant statistical reliability and high predictive ability after internal and external validation. By analyzing the contour maps, the electrostatic and hydrophobic interactions were found to be crucial for improving the inhibitory activity and four novel tubulin inhibitors (Compounds D1, D2, D3, and D4) were designed based on the validated 3D-QSAR models. Moreover, the docking findings showed that residues Gln136, Val238, Thr239, Asn167, Val 318 and Ala 316 played important roles for quinoline binding to tubulin. Among the newly designed compounds, compound D1 with the highest total scoring was subjected to detailed molecular dynamics (MD) simulation and compared to the most active compound. The conformational stability of compound D1 complexed with tubulin protein was confirmed by a 50-ns molecular dynamics simulation, which was congruent with molecular docking. Resumen. La tubulina juega un papel indispensable en la regulación de varios procesos celulares importantes. Recientemente, se le ha reconicodo como un agente promisorio para atacar la rápida división de las células cancerosas. Últimamente se ha identificado una nueva serie de derivados de arilaminotiazo-2-oxoquinolina como potenciales inhibidores de la tubulina, con una elevada actividad citotóxica contra la línea celular de cáncer HeLa. En este estudio, se aplicó a los derivados informados un estudio 3D-QSAR mediante el uso de técnicas CoMFA y CoMSIA para comprender los factores farmacológicos que contribuyen a la actividad como inhibidor y selectivo de la tubulina. Se encontró que los modelos CoMFA y CoMSIA óptimos tienen una confiabilidad estadística significativa y una alta capacidad predictiva después de la validación interna y externa. Al analizar los mapas de contorno, se descubrió que las interacciones electrostáticas e hidrófobas eran cruciales para mejorar la actividad inhibidora y se diseñaron cuatro nuevos inhibidores de la tubulina (compuestos D1, D2, D3 y D4) basados en los modelos 3D-QSAR validados. Además, los hallazgos de acoplamiento mostraron que los residuos Gln136, Val238, Thr239, Asn167, Val 318 y Ala 316 desempeñaron papeles importantes en la unión de la quinolina a la tubulina. Entre los compuestos de nuevo diseño, el compuesto D1 con la puntuación total más alta se sometió a una simulación detallada de dinámica molecular (MD) y se comparó con el compuesto más activo. La estabilidad conformacional del compuesto D1 unido a la proteína tubulina se confirmó mediante una simulación de dinámica molecular de 50 ns, que fue congruente con el acoplamiento molecular.

In silico modelling studies were executed on thirty two N-substituted oseltamivir derivatives as inhibitors of influenza virus H5N1. Robust validated quantitative structure–activity relationship (QSAR) approaches have been investigated to explore the important structural requirements essential to design potent anti-influenza virus H5N1 inhibitors. Density functional theory (DFT) calculations with the Becke three-parameter Lee–Yang–Parr B3LYP hybrid functional employing the 6-31G(d) basis set are used to calculate quantum chemical descriptors. The dataset was randomly divided into training and test sets comprising 25 and 7 compounds, respectively. Twenty models were established by changing the compounds of the sets and further were applied to calculate the predicted pIC50 values of the 7 compounds in the test set. All constructed models were individually validated internally as well as externally along with y-randomization according to the OECD principles for QSAR model validation and Golbraikh and Tropsha's criteria of model acceptance. Model 5 is selected with higher R2, Rtest2 and Qcv2 values (R2 = 0.902, Radj2 = 0.888, MSE = 0.094, Rtest2 = 0.872, Qcv2 = 0.857). It is very interesting to find that the anti-influenza H5N1 activity of these compounds appears to be mainly governed by three molecular descriptors, i.e. the lowest unoccupied molecular orbital energy (ELUMO), the energy of the molecular orbital below the HOMO (EHOMO−1) and the number of atoms (NA). Here the possible mechanism of action of these compounds was analysed and discussed; in particular, important structural requirements for enhanced H5N1 virus inhibitor activity could be reached by reducing the molecular size and introducing stronger electron accepting ability groups with small atoms and more protons into the N-substituted oseltamivir derivatives. Based on the best proposed QSAR model, some new compounds with higher neuraminidase inhibitor activity have been theoretically designed. Such results can offer useful theoretical references for future experimental work.

In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods.The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

Because of the rapid increase in deaths due to Staphylococcus aureus (S. aureus) infection, significant resources have been invested over the past decade in new treatments. Successful therapy requires amalgam of therapies to delineate the pathogen while providing protection to the host cell. With this idea, indole-pyrimidine hybrids have been used to develop new antibacterial agents. This heterocyclic has a fundamental role in medicinal chemistry and serves as a key model for the development of various therapeutic agents including broad-spectrum antibacterial drugs. In this study, we have employed combined studies of Three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking which are validated by in silico ADME prediction; those methods have been performed on indole-pyrimidine hybrids against S. aureus. 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) with Q2 of 0.560, R2 of 0.925, and Comparative Molecular Similarity Indices Analysis (CoMSIA) with Q2 of 0.577, R2 of 0.876. The predictive ability of these models was determined using a test set of molecules that gave acceptable predictive correlation (R2test) values 0.729 and 0.737 of CoMFA and CoMSIA respectively. Developed models and Docking methods provide guidance to design molecules with enhanced activity.

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 this paper, we present a theoretical analysis of the molecular structure of a conjugated molecule TTPA-TTF at the DFT level using the B3LYP method and the 6-31G (d) basis set. The TTPA-TTF mole-cule presented a twisted configuration, which gave it good solubility in different organic solvents. The Partial atomic charge, molecular electrostatic potential (MEP) map, and global reactivity descriptors highlight the reactive sites of the molecule with the possible prediction of its reactivity. Moreover, a clear image of the intra- and intermolecular interactions illustrates hyperconjugative interactions based on the charge delocalization that emerges from the natural bond orbital analysis. The non-linear optical proper-ties of the TTPA-TTF molecule can also be calculated by determining their first hyperpolarizabilities. The time-dependent density theory method TD-DFT-B3LYP 6-31G (d) was used for the study of absorption. The obtained results show a broad spectrum in the visible range favorable to harvest solar light.

Acetylcholinesterase (AChE) is a potential target for the development of small molecules as inhibitors for the therapy of Alzheimer’s disease (AD). To design highly active acetylcholinesterase inhibitors, a three-dimensional quantitative structure–activity relationship (3D-QSAR) approach was performed on a series of N-benzylpyrrolidine derivatives previously evaluated for acetylcholinesterase inhibitory activity. The developed two models, CoMFA and CoMSIA, were statistically validated, and good predictability was achieved for both models. The information generated from 3D-QSAR contour maps may provide a better understanding of the structural features required for acetylcholinesterase inhibition and help to design new potential anti-acetylcholinesterase molecules. Consequently, six novel acetylcholinesterase inhibitors were designed, among which compound A1 with the highest predicted activity was subjected to detailed molecular docking and compared to the most active compound. Extra-precision molecular dynamics (MD) simulation of 50 ns and binding free energy calculations using MM-GBSA were performed for the selected compounds to validate the stability. These results may afford important structural insights needed to identify novel acetylcholinesterase inhibitors and other promising strategies in drug discovery.

Coronavirus (COVID-19), an enveloped RNA virus, primarily affects human beings. It has been deemed by the World Health Organization (WHO) as a pandemic. For this reason, COVID-19 has become one of the most lethal viruses which the modern world has ever witnessed although some established pharmaceutical companies allege that they have come up with a remedy for COVID-19. To that end, a set of carboxamides sulfonamide derivatives has been under study using 3D-QSAR approach. CoMFA and CoMSIA are one of the most cardinal techniques used in molecular modeling to mold a worthwhile 3D-QSAR model. The expected predictability has been achieved using the CoMFA model (Q2 = 0.579; R2 = 0.989; R2test = 0.791) and the CoMSIA model (Q2 = 0.542; R2 = 0.975; R2test = 0.964). In a similar vein, the contour maps extracted from both CoMFA and CoMSIA models provide much useful information to determine the structural requirements impacting the activity; subsequently, these contour maps pave the way for proposing 8 compounds with important predicted activities. The molecular surflex-docking simulation has been adopted to scrutinize the interactions existing between potentially and used antimalarial molecule on a large scale, called Chloroquine (CQ) and the proposed carboxamides sulfonamide analogs with COVID-19 main protease (PDB: 6LU7). The outcomes of the molecular docking point out that the new molecule P1 has high stability in the active site of COVID-19 and an efficient binding affinity (total scoring) in relation with the Chloroquine. Last of all, the newly designed carboxamides sulfonamide molecules have been evaluated for their oral bioavailability and toxicity, the results point out that these scaffolds have cardinal ADMET properties and can be granted as reliable inhibitors against COVID-19., SARS-CoV-2, 3D-QSAR, carboxamides sulfonamide, Molecular docking, drug discovery, in silico ADMET.

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected whole the world with more than 6 million confirmed cases and over 370,000 deaths. At present, there are no effective treatments or vaccine for this disease, which constitutes a serious global health crisis. As the pandemic still spreading around the globe, it is of interest to use computational methods to identify potential inhibitors for the virus. The crystallographic structures of 3CLpro (PDB: 6LU7) and RdRp (PDB 6ML7) were used in virtual screening of 50000 chemical compounds obtained from the CAS Antiviral COVID19 database using 3D-similarity search and standard molecular docking followed by ranking and selection of compounds based on their binding affinity, computational techniques for the sake of details on the binding interactions, absorption, distribution, metabolism, excretion, and toxicity prediction; we report three 4-(morpholin-4-yl)-1,3,5-triazin-2-amine derivatives; two compounds (2001083-68-5 and 2001083-69-6) with optimal binding features to the active site of the main protease and one compound (833463-19-7) with optimal binding features to the active site of the polymerase for further consideration to fight COVID-19. The structural stability and dynamics of lead compounds at the active site of 3CLpro and RdRp were examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the three complexes remain stable during simulation of 20 ns, which may be suitable candidates for further experimental analysis. As the identified leads share the same scaffold, they may serve as promising leads in the development of dual 3CLpro and RdRp inhibitors against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Introduction: The new species of coronaviruses (CoVs), SARS-CoV-2, was reported as responsible for an outbreak of respiratory disease. Scientists and researchers are endeavoring to develop new approaches for the effective treatment against of the COVID-19 disease. There are no finally targeted antiviral agents able to inhibit the SARS-CoV-2 at present. Therefore, it is of interest to investigate the potential uses of levamisole derivatives, which are reported to be antiviral agents targeting the influenza virus. Methods: In the present study, 12 selected levamisole derivatives containing imidazo[2,1-b]thiazole were subjected to molecular docking in order to explore the binding mechanisms between these derivatives and the SARS-CoV-2 Mpro (PDB: 7BQY). The levamisole derivatives were evaluated for in silico ADMET properties for wet-lab applicability. Further, the stability of the best-docked complex was checked using molecular dynamics (MD) simulation at 20 ns. Results: Levamisole derivatives and especially molecule N°6 showed more promising docking results, presenting favorable binding interactions as well as better docking energy compared to chloroquine and mefloquine. The results of ADMET prediction and MD simulation support the potential of the molecule N°6 to be further developed as a novel inhibitor able to stop the newly emerged SARS-CoV-2. Conclusion: This research provided an effective first line in the rapid discovery of drug leads against the novel CoV (SARS-CoV-2).

The new Corona-virus, recently called the severe acute respiratory syndrome Coronavirus (SARS-CoV-2) appears for the first time in China and more precisely in Wuhan (December 2019). This disease can be fatal. Seniors, and people with other medical conditions (diabetes, heart disease…), may be more vulnerable and become seriously ill. This is why research into drugs to treat this infection remains essential in several research laboratories. Natural herbal remedies have long been the main, if not the only, remedy in the oral tradition for treating illnesses. Modern medicine has known its success thanks to traditional medicine, the effectiveness of which derives from medicinal plants. The objective of this study is to determine if the components of natural origin have an anti-viral effect and which can prevent humans from infection by this coronavirus using the most reliable method is molecular docking, which used to find the interaction between studied molecules and the protein, in our case we based on the inhibitor of Coronavirus (nCoV-2019) main protease. The results of molecular docking showed that among 67 molecules of natural origin, three molecules (Crocin, Digitoxigenin, and β-Eudesmol) are proposed as inhibitors against the coronavirus based on the energy types of interaction between these molecules and studied protein. Communicated by Ramaswamy H. Sarma Highlights Determine natural compounds that can have an anti-viral effect and which can prevent humans from infection by this coronavirus; Molecular docking to find interaction between the molecules studied and the receptor of COVID-19; The synthesis of these molecules and the evaluation of their in vitro activity against SARS-Cov-2 could be interesting.