Showing papers in "Structural Chemistry in 2022"

Antiviral phytocompounds “ellagic acid” and “(+)-sesamin” of Bridelia retusa identified as potential inhibitors of SARS-CoV-2 3CL pro using extensive molecular docking, molecular dynamics simulation studies, binding free energy calculations, and bioactivity prediction

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected billions and has killed millions to date. Studies are being carried out to find therapeutic molecules that can potentially inhibit the replication of SARS-CoV-2. 3-chymotrypsin-like protease (3CL pro) involved in the polyprotein cleavage process is believed to be the key target for viral replication, and hence is an attractive target for the discovery of antiviral molecules. In the present study, we aimed to identify natural phytocompounds from Bridelia retusa as potential inhibitors of SARS-CoV-2 3CL pro (PDB ID: 6M2N) using in silico techniques. Molecular docking studies conducted with three different tools in triplicates revealed that ellagic acid (BR6) and (+)-sesamin (BR13) has better binding affinity than the co-crystal inhibitor “3WL” of 6M2N. BR6 and BR13 were found to have a high LD50 value with good bioavailability. 3WL, BR6, and BR13 bind to the same active binding site and interacted with the HIS41-CYS145 catalytic dyad including other crucial amino acids. Molecular dynamics simulation studies revealed stability of protein–ligand complexes as evidenced from root-mean-square deviations, root-mean-square fluctuations (RMSF), protein secondary structure elements, ligand-RMSF, protein–ligand contacts, ligand torsions, and ligand properties. BR6 (−22.3064 kcal/mol) and BR13 (−19.1274 kcal/mol) showed a low binding free energy value. The Bayesian statistical model revealed BR6 and BR13 as better protease inhibitors than 3WL. Moreover, BR6 and BR13 had already been reported to elicit antiviral activities. Therefore, we conclude that ellagic acid and (+)-sesamin as natural antiviral phytocompounds with inhibitory potential against SARS-CoV-2 3CL pro.

3D-QSAR, ADME-Tox, and molecular docking of semisynthetic triterpene derivatives as antibacterial and insecticide agents

Abstract: In the present work, 27 triterpene derivatives have been subjected to 3D-QSAR, ADME-Tox, and molecular docking for their insecticidal activity. The selected derivatives are previously semi-synthesized based on compounds obtained from Euphorbia resinifera and Euphorbia officinarum latex. The in silico studies were used to predict and to evaluate the antibacterial and insecticidal properties of the 3D structure of triterpene derivatives. The 3D-QSAR models are developed using CoMFA and CoMSIA techniques, and they have showed excellent statistical results (R2 = 0.99; Q2 = 0.672; R2pred = 0.91 for CoMFA and R2 = 0.97; Q2 = 0.61; R2pred = 0.94 for CoMSIA). The results indicate that the built models are able to describe the relationship between the structure of triterpene derivatives and the pLD50 bioactivity. Based on contour maps obtained from CoMFA and CoMSIA models, 38 new molecules are designed and their pLD50 activities are predicted. The drug-like and ADME-Tox properties of the molecule designed are examined and led to the selection of four molecules (55, 56, 59, 64) as promising antibacterial and insecticidal agents. Compounds 55, 56, 59, and 64 are able to inhibit the MurE (PDB code: 1E8C) and EcR (PDB code: 1R20) proteins involved in the process of antibacterial and insecticidal activities. This hypothesis is confirmed by the implementation of a molecular docking test. This test predicted the most important referential interactions that occur between the structure of triterpene derivatives and the targeted receptors. Among the four docked molecules, three molecules (55, 56, and 59) showed greater stability than the reference molecule 16 inside the MurE and EcR receptors pocket. Therefore, the structure of the three new triterpene derivatives can be adopted as reference for the synthesis of antibacterial drugs and also in the development of insecticides.

A comprehensive exploration of pharmacological properties, bioactivities and inhibitory potentiality of luteolin from Tridax procumbens as anticancer drug by in-silico approach

Abstract: Tridax procumbens is a flowering plant of the Asteraceae family with a wide range of medicinal uses like anti-inflammatory, anti-diabetic, anti-microbial, immunomodulatory, etc. This study aimed to investigate the anti-cancerous activity of human lung cancer for targeting luteolin, a phytochemical of Tridax procumbens. The computational study has been done for studying the structural properties of luteolin. The drug-likeness of the molecule has been predicted by virtual screening of ADMET properties. The molecular docking technique of the in-silico method is performed to check the complex formation between protein and ligand. The reactivity and stability of the molecule are investigated with the help of molecular dynamics (MD) simulations. In the present work, we have tried to establish a strong candidature of any of the phytochemical of Tridax Procumbens as an inhibitor against human lung cancer.The online version contains supplementary material available at 10.1007/s11224-022-01882-7.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach

Abstract: Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CLpro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CLpro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure–activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R2 = 0.97, Q2 = 0.81, R2pred = 0.95, cR2p = 0.71) and CoMSIA/SEHDA (R2 = 0.94, Q2 = 0.76, R2pred = 0.91, cR2p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC50 = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CLpro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CLpro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19.

In silico bioprospecting of taraxerol as a main protease inhibitor of SARS-CoV-2 to develop therapy against COVID-19

Abstract: COVID-19 was caused by a novel coronavirus known as SARS-CoV-2. The COVID-19 disease outbreak has been avowed as a global pandemic by the World Health Organization at the end of March 2020. It leads to the global economic crash, resulting in the starvation of a large population belonging to economically backward countries. Hence, the development of an alternative medicine along with the vaccine is of the utmost importance for the management of COVID-19. Therefore, screening of several herbal leads was performed to explore their potential against SARS-CoV-2. Furthermore, viral main protease was selected as a key enzyme for performing the study. Various computational approaches, including molecular docking simulation, were used in the current study to find potential inhibitors of viral main protease from a library of 150 herbal leads. Toxicity and ADME prediction of selected molecules were also analysed by Osiris molecular property explorer software. Molecular dynamic simulation of the top 10 docked herbal leads was analysed for stability using 100 ns. Taraxerol (-10.17 kcal/mol), diosgenin (10.12 kcal/mol), amyrin (-9.56 kcal/mol), and asiaticoside (-9.54 kcal/mol) were among the top four herbal leads with the highest binding affinity with the main protease enzyme. Thus, taraxerol was found to be an effective drug candidate against the main protease enzyme for the management of COVID-19. Furthermore, its clinical effect and safety profile need to be established through an in vivo model.The online version contains supplementary material available at 10.1007/s11224-022-01943-x.

Study of nonlinear optical responses of phytochemicals of Clitoria ternatea by quantum mechanical approach and investigation of their anti-Alzheimer activity with in silico approach

Abstract: Clitoria ternatea is a flowering plant with promising medicinal plants with a wide variety of active phytochemicals. The present study aimed at the computational investigation of the nonlinear optical (NLO) responses of the active phytochemicals of the Clitoria ternatea. The computational investigation of the NLO features was done by using the density functional theory (DFT) by B3LYP/6-311G + + (d, p) basis set. The structural parameters, mulliken charge distribution, and molecular electrostatic potential (MEP) surface clearly show the intramolecular charge transfer within Clitorin. The NLO properties were identified by computing the polarizability parameters. As the plant has high medicinal characteristics, the inhibiting properties of its phytochemicals were also investigated to combat Alzheimer disease (AD). The systematic in silico study identifies Clitorin as the most active and inhibiting phytochemicals of the plant. The results obtained from molecular dynamics (MD) simulation tell the stability of the complex and make it a fair selection as a drug-like molecule against AD. The cardio-toxicity analysis done for the Clitorin molecule verifies that it is harmless for the heart.The online version contains supplementary material available at 10.1007/s11224-022-01981-5.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection

Abstract: The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates.The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

In silico docking and ADME study of deketene curcumin derivatives (DKC) as an aromatase inhibitor or antagonist to the estrogen-alpha positive receptor (Erα+): potent application of breast cancer

Abstract: Regardless of many extensive studies, hormonal-based breast cancer is the most common cause of cancer-related mortality of females worldwide. Indeed, estrogen receptor-positive (ER +) is the communal subtype in breast cancer. To treat this, three types of medications are typically used: selective estrogen receptor modulators (SERMs), selective estrogen receptor down modulators (SERDMs), and aromatase inhibitors (AIs), all of which directly interact with the activation of the estrogen signaling pathway and its formation. Despite their effectiveness, the development of new treatments is required since clinical efficacy is restricted owing to resistance. As a result, in silico studies for drug discovery are booming over the decades because of their affordability and less time-consuming features. Here, 25 deketene curcumin derivatives have been selected for docking studies through MVD software over the positive type of breast cancer through both the treatment hosts Erα + receptor and aromatase. DKC compounds are used because they have several pharmacological uses, including anti-cancer, anti-diabetic, anti-viral, anti-fungal, and anti-bacterial properties. Moreover, an ADME study was carried out for DKC derivatives that reveal the optimum drug-likeness profile. From 25 derivatives, the results showed a better MolDock score, hydrogen bonding, and steric interaction between compounds DKC-10, DKC-20, and DKC-21 with Erα + and aromatase. Although the study was done on both the treatable path hosts, better results were obtained with Erα + as an antagonist. Therefore, it is proposed that three selected DKC derivatives would be better therapeutic agents against breast cancer.The online version contains supplementary material available at 10.1007/s11224-021-01871-2.

In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations

Abstract: In the present study, a new category of 1,3,4-thiadiazoles was developed by submitting methyl 2-(4-hydroxy-3-methoxybenzylidene) hydrazine-1-carbodithioate to react with the appropriate hydrazonoyl halides in presence of few drops of diisopropyl ethyl amine. The chemical structures of the newly synthesized derivatives were inferred by means of their micro-analytical and spectral data. Utilizing combined molecular docking and molecular dynamics techniques, the binding affinities and features of the synthesized compounds were evaluated against four SARS-CoV-2 target enzymes, namely, main protease (Mpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), and receptor-binding domain (RBD) of the spike protein. Compound 7 demonstrated promising binding affinities with the target enzymes Mpro, PLpro, RdRp, and RBD with docking scores of -11.4, -9.4, -8.2, and -6.8 kcal/mol, respectively. In addition, compound 7 exhibited MM-GBSA//100 ns MD docking score of -35.9 kcal/mol against Mpro. Structural and energetic analyses revealed the stability of the 7-Mpro complex over 100 ns MD simulations. In addition, compound 7 obeyed Lipinski's rule of five, as it has acceptable absorption, distribution, and oral bioavailability inside the body. Therefore, compound 7 is considered as a promising starting point for designing potential therapeutic agents against Covid-19.The online version contains supplementary material available at 10.1007/s11224-022-01985-1.

In silico analyses of betulin: DFT studies, corrosion inhibition properties, ADMET prediction, and molecular docking with a series of SARS-CoV-2 and monkeypox proteins

Abstract: We report detailed computational studies of betulin - a pentacyclic naturally occuring triterpene, which is a precursor for a broad family of biologically active derivatives. The structure, electronic, and optical properties of betulin were studied by the density functional theory (DFT) calculations in gas phase. The reactivity and the reactive centers of betulin were revealed through its global reactivity descriptors and molecular electrostatic potential (MEP). The DFT calculations were also applied to probe betulin as a potential corrosion inhibitor for some important metals used in implants. Electron charge transfer from the molecule of betulin to the surface of all the examined metals (Ti, Fe, Zr, Co, Cu, Cr, Ni, Mn, Mo, Zn, Al, W, Ag, Au) was revealed, of which the best results were obtained for Ni, Au and Co. Bioavailability, druggability as well as absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of betulin were evaluated using the SwissADME, BOILED-Egg and ProTox-II tools. Molecular docking was applied to examine the influence of the title compound on a series of the SARS-CoV-2 proteins as well as one of the monkeypox proteins. It was established that betulin is active against all the applied proteins with the best binding affinity with papain-like protease (PLpro) and spike protein (native) of SARS-CoV-2. The title compound is also active against the studied monkeypox protein. Interaction of betulin with papain-like protease (PLpro) was studied using molecular dynamics simulations.

Perceiving SARS-CoV-2 Mpro and PLpro dual inhibitors from pool of recognized antiviral compounds of endophytic microbes: an in silico simulation study

Abstract: Coronavirus disease 2019 (COVID-19) persists and shook the global population where the endgame to this pandemic is brought on by developing vaccines in record-breaking time. Nevertheless, these vaccines are far from perfect where their efficiency ranges from 65 to 90%; therefore, vaccines are not the one only solution to overcome this situation, and apart from administration of vaccines, the scientific community is at quest for finding alternative solutions to incumber SARS-CoV-2 infection. In this study, our research group is keen on identifying a bioactive molecule that is independent in its mode of action from existing vaccines which can potentially target the SARS-CoV-2 virus replicative efficacy. Papain-like protease (PLpro) and main protease (Mpro) are the most lucrative targets of COVIDs against which the drugs can be developed, as these proteases play a vital role in the replication and development of viral particles. Researchers have modelled a compound such as GRL0617 and X77 as an inhibitor of Mpro and PLpro, respectively, but use of these compounds has several limitations on hosts like toxicity and solubility. Under the current study by deploying rigorous computational assessments, pool of microbial secondary metabolites was screened and handpicked to search a structural or functional analogue of GRL0617 and X77, with an idea to identify a compound that can serve as dual inhibitor for both PLpro and Mpro. From the manually curated database of known antiviral compounds from fungal origin, we found cytonic acids A and B to potentially serve as dual inhibitor of PLpro and Mpro.

Consensus docking and MM-PBSA computations identify putative furin protease inhibitors for developing potential therapeutics against COVID-19

Abstract: The coronavirus disease 2019 (COVID-19) is a pandemic that has severely posed substantial health challenges and claimed millions of lives. Though vaccines have been produced to stem the spread of this disease, the death rate remains high since drugs used for treatment have therapeutic challenges. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease, has a slew of potential therapeutic targets. Among them is the furin protease, which has a cleavage site on the virus's spike protein. The cleavage site facilitates the entry of the virus into human cells via cell-cell fusion. This critical involvement of furin in the disease pathogenicity has made it a viable therapeutic strategy against the virus. This study employs the consensus docking approach using HYBRID and AutoDock Vina to virtually screen a pre-filtered library of 3942 natural product compounds of African origin against the human furin protease (PDB: 4RYD). Twenty of these compounds were selected as hits after meeting molecular docking cut-off of - 7 kcal.mol-1, pose alignment inspection, and having favorable furin-ligand interactions. An area under the curve (AUC) value of 0.72 was computed from the receiver operator characteristic (ROC) curve, and Boltzmann-enhanced discrimination of the ROC curve (BEDROC) value of 0.65 showed that AutoDock Vina was a reasonable tool for selecting actives for this target. Seven of these hits were proposed as potential leads having had bonding interactions with catalytic triad residues Ser368, His194, and Asp153, and other essential residues in the active site with plausible binding free energies between - 189 and - 95 kJ/mol from the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations as well as favorable ADME/Tox properties. The molecules were also predicted as antiviral, anti-inflammatory, membrane permeability inhibitors, RNA synthesis inhibitors, cytoprotective, and hepatoprotective with probable activity (Pa) above 0.5 and probable inactivity values below 0.1. Some of them also have anti-influenza activity. Influenza virus has many similarities with SARS-CoV-2 in their mode of entry into human cells as both are facilitated by the furin protease. Pinobanksin 3-(E)-caffeate, one of the potential leads is a propolis compound. Propolis compounds have shown inhibitory effects against ACE2, TMPRSS2, and PAK1 signaling pathways of SARS-CoV-2 in previous studies. Likewise, quercitrin is structurally similar to isoquercetin, which is currently in clinical trials as possible medication for COVID-19.The online version contains supplementary material available at 10.1007/s11224-022-02056-1.

Effect of carboxylic acid and cyanoacrylic acid as anchoring groups on Coumarin 6 dye for dye-sensitized solar cells: DFT and TD-DFT study

Abstract: Abstract Starting with Coumarin-6 dye, two novel D-π-A organic dyes C6X and C6N have been designed by attaching carboxylic acid and cyanoacrylic acid groups as anchoring groups to Coumarn-6 dye, respectively, to understand their potential use in dye-sensitized solar cells (DSSCs). The electronic structure and photophysical and photovoltaic properties of the novel designed dyes were studied using density functional theory DFT and time-dependent density functional theory TD-DFT with the Becke3-Parameter-Lee–Yang–Parr (B3LYP) functional and the 6-31G (d, p) basis set. Optimized structure and electronic properties (highest occupied molecular orbital energy (E HOMO ), lowest unoccupied molecular orbital (E LUMO ), and energy difference (E g ) between HOMO and LUMO) were calculated showing that C6N has the smallest band gap with the larger absorption region. Density of states (DOS), molecular electrostatic potential (MEP), natural bond orbital (NBO) analysis, non-linear optical (NLO) properties, UV–vis spectra, as well as some crucial parameters affecting the photovoltaic performance of DSSCs, such as light-harvesting efficiency (LHE), electron injection driving force (ΔG inject ), dye regeneration driving force(ΔG reg ), and the excited state life time(τ e ), were calculated to study the effect of the anchoring group on the DSSC performance. Additionally, the adsorption of C6X and C6N dyes on the TiO2 anatase (101) surface and the mechanism of electron injection were also investigated using a dye–(TiO 2 ) 9 cluster model using TD-B3LYP calculation. The calculated adsorption energies of the dyes suggest a strong adsorption of dyes to a TiO 2 surface. The results show that C6N may be theoretically a good candidate as sensitizer of DSSC application.

The system of self-consistent QSPR-models for refractive index of polymers

Abstract: Quantitative structure–property/activity relationships (QSPRs/QSARs) are a component of modern natural science. The system of self-consistent models is a specific approach to build up QSPR/QSAR. A group of models of refractive index for different distributions in training and test sets is compared. This comparison is a basis to formulate the system of self-consistent models. The so-called index of ideality of correlation (IIC) has been used to improve the predictive potential of models of the refractive index of different polymers (n = 255). The predictive potential of the suggested models is high since the average value of the determination coefficient for the validation set is 0.885. In addition, the system of self-consistent models may be applied as a tool to assess the predictive potential of an arbitrary QSPR-approach. The statistical characteristics of the best model are the following: n = 57, R2 = 0.7764, RMSE = 0.039 (active training set) and n = 57, R2 = 0.9028, RMSE = 0.019 (validation set).

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

Abstract: The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Unveiling the synthesis of spirocyclic, tricyclic, and bicyclic triazolooxazines from intramolecular [3 + 2] azide-alkyne cycloadditions with a molecular electron density theory perspective

Abstract: The intramolecular [3+2] cycloaddition (32CA) reactions of azido alkynes leading to spirocyclic, tricyclic, and bicyclic triazolooxazines has been studied within the molecular electron density theory (MEDT) at the MPWB1K/6-311G(d,p) level. The electron localization function (ELF) characterizes the azido alkynes as zwitterionic species. Analysis of the conceptual DFT indices allows classifying the azide moiety as the electrophilic counterpart and the alkyne as the nucleophilic one. These 32CA reactions are under kinetic control with the activation free energies of 23.4–26.7 kcal mol−1. Along the reaction path, the pseudoradical centre is created initially at C4, consistent with the Parr function analysis; however, the sequence of bond formation is controlled by the energetically feasible formation of the six-membered oxazine ring. The intermolecular interactions at the transition states were characterized from the quantum theory of atoms in molecules (QTAIM) study and the non-covalent interaction (NCI) gradient isosurfaces.

Structurally modulated D-π-D-A(Semiconductor) anchoring dyes to enhance the tunable NLO response: a DFT/TDDFT quest for new photovoltaic materials

Abstract: In this paper, we designed new dyes (D-1 to D-5) with anchoring groups to test their stability for semiconductor surfaces with silyl unit as dye-sensitized solar cells (DSSCs). To investigate their photovoltaic efficiency, density functional theory (DFT) calculations were conducted with these novel D-π-D-A(Semiconductor) type structures using N,N-dimethylaniline and benzenesulfonate as electron donor (D) and a thiophene as π-conjugated spacers, with different semiconductor units as anchoring and electron acceptor units. A new dye structure as a reference (Ref-D) had been extended from methyl orange (MO) molecular structure with electron acceptor semiconducting units to improve the electronic transmission and increased maximum absorbance (λmax) to derive these new dyes (D-1 to D-5). The computed λmax of MO was obtained by testing DFT functional to benchmark it with its experimental λmax. Out of these functionals, the Coulomb-attenuating Becke, 3-parameter, Lee–Yang–Parr (CAM-B3LYP) functional having hybrid and long range correlation with 6-31G + (d,p) produced a nearly similar λmax (459 nm) as of its experimental one (464 nm). Their ionization potentials (I1) ranged between 2.65 and 5.31 eV which showed their good electron-donating nature. Their λmax values ranged between 532 and 565 nm which had a considerable red shift from Ref-D (465 nm). The highest second-order nonlinear optical (NLO) response of 134,532 Debye-Angstrom−1 was noted for dye D-2 which had the shortest bandgap. The charge tripping analysis of all the dyes miscible with the Ref-D showed an exclusive shift from highest occupied molecular orbital (HOMO) of reference to lowest unoccupied molecular orbital (LUMO) of dye. The density of states (DOS) calculations were performed with the dye D-5 to show that electronic transmission was from the dye towards the semiconductor in an efficient way. The inclusion of thiophene as π-conjugated spacer resulted in a significant increase in absorbance peak at around 80 nm. The DFT computed results offered an insight upon design of novel DSSCs with silyl anchoring groups for improved stability and efficiency. The present research is in a kind of prediction to develop new NLO materials with D-π-A design involving semiconductor as anchoring groups to attach with a DSSC surface.

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

Abstract: A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS‐CoV‐2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.