Designing Thiadiazoloquinoxaline-Based Conjugated Polymers for Efficient Organic Photovoltaics: A DFT/TDDFT Study

Clean and renewable energy development is becoming frontier research for future energy resources, as renewable energy offers sustainable and environmentally friendly alternatives to non-renewable sources such as fossil fuels. Among various renewable energy sources, tremendous progress has been made in converting solar energy to electric energy by developing efficient organic photovoltaics. Organic photovoltaic materials comprising conjugated polymers (CP) with narrow optical energy gaps are promising candidates for developing sustainable sources due to their potentially lower manufacturing costs. Organic semiconductor materials with a high electron affinity are required for many optoelectronic applications. We have designed a series of organic semiconductors comprised of cyclopentadithiophene as a donor and thiadiazoloquinoxaline (TQ) as an acceptor, varying the π-conjugation and TQ-derivatives. We have employed density functional theory (DFT) and time-dependent DFT (TDDFT) to evaluate the designed CP's optoelectronic properties, such as optical energy gap, dipole moment, and absorption spectra. Our DFT/TDDFT result shows that the energy gap of CPs is lowered and redshifted in the absorption spectra if there is no insertion of conjugation units such as thiophene and selenophene between donor and acceptor. In addition, selenophene shows relatively better redshift behavior compared to thiophene. Our work also provides rational insight into designing donor/acceptor-based CPs for organic solar cells.

Dissociation Time, Quantum Yield, and Dynamic Reaction Pathways in the Thermolysis of trans-3,4-Dimethyl-1,2-dioxetane

The thermolysis of trans-3,4-dimethyl-1,2-dioxetane is studied by trajectory surface hopping. The significant difference between long and short dissociation times is rationalized by frustrated dissociations and the time spent in triplet states. If the C-C bond breaks through an excited state channel, then the trajectory passes over a ridge of the potential energy surface of that state. The calculated triplet quantum yields match the experimental results. The dissociation half-times and quantum yields follow the same ascending order as per the product states, justifying the conjecture that the longer dissociation time leads to a higher quantum yield, proposed in the context of the methylation effect. The populations of the molecular Coulomb Hamiltonian and diagonal states reach equilibrium, but the triplet populations with different Sz components fluctuate indefinitely. Certain initial velocities, leading the trajectories to given product states, can be identified as the most characteristic features for sorting trajectories according to their product states.

Degradation of NTO induced by superoxide and hydroperoxyl radicals: a comprehensive DFT study

Reactive oxygen species, produced in the aquatic environment under sunlight irradiation, actively take part in degradation of environmental pollutants. NTO (5-nitro-1,2,4-triazol-3-one), being a primary ingredient in a suite of insensitive munitions formulations, may be released into training range soils after incomplete detonations and dissolved in surface water and groundwater due to good water solubility. A detailed investigation of a possible mechanism for NTO decomposition in water induced by superoxide and hydroperoxyl radicals as one of the pathways for NTO environmental degradation was performed with a computational study at the PCM(Pauling)/M06-2X/6-311++G(d,p) level. Superoxide causes rapid deprotonation of NTO. Decomposition of NTO induced by hydroperoxyl radicals was found to be a multistep process leading to mineralization of the nitrocompound. The reaction process may begin with hydroperoxyl radical attachment to carbon atom of the CN double bond of NTO, then proceeds through rupture of C-N bonds and addition of water molecules leading to the formation of nitrous acid, ammonia, nitrogen gas, hydrazine, and carbon(IV) oxide. The obtained results indicate that the anionic form of NTO shows a higher reactivity towards hydroperoxyl radicals than its neutral form. Excitation of NTO by sunlight enables complete mineralization of NTO induced by superoxide. The calculated activation energies and exergonicity of the studied processes support the contribution of hydroperoxyl radicals and superoxide to the degradation of NTO in the environment into low-weight inorganic compounds.

Ultrahigh Capacitive Energy Density in Stratified 2D Nanofiller-Based Polymer Dielectric Films

Dielectric capacitors are critical components in electronics and energy storage devices. The polymer-based dielectric capacitors have the advantages of device flexibility, fast charge-discharge rates, low loss, and graceful failure. Elevating the use of polymeric dielectric capacitors for advanced energy applications such as electric vehicles (EVs), however, requires significant enhancement of their energy densities. Here, we report a polymer thin film heterostructure-based capacitor of poly(vinylidene fluoride)/poly(methyl methacrylate) with stratified 2D nanofillers (Mica or h-BN nanosheets) (PVDF/PMMA-2D fillers/PVDF), that shows enhanced permittivity, high dielectric strength, and an ultrahigh energy density of ≈75 J/cm3 with efficiency over 79%. Density functional theory calculations verify the observed permittivity enhancement. This approach of using oriented 2D nanofillers-based polymer heterostructure composites is expected to be versatile for designing high energy density thin film polymeric dielectric capacitors for myriads of applications.

The System of Self-Consistent Models: The Case of Henry's Law Constants

Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).

Role of Hydroxyl Radical in Degradation of NTO: DFT Study

Hydroxyl radicals are important reactive oxygen species produced in the aquatic environment under sunlight irradiation. Many organic pollutants may be decomposed as they encounter hydroxyl radicals, due to their high oxidative ability. NTO (5-nitro-1,2,4-triazol-3-one), an energetic material used in military applications, may be released to the environment and dissolved in surface water and groundwater due to its good water solubility. A detailed investigation of the possible mechanism for NTO decomposition in water induced by hydroxyl radical as one of the pathways for NTO environmental degradation was performed by computational study at the PCM/M06-2X/6-311++G(d,p) level. Decomposition of NTO was found to be a multistep process that may begin with an addition of hydroxyl radical to the carbon atom of C═N double bond and consequent release of a nitrite radical. The formed intermediate undergoes a series of chemical transformations that include the attachments of hydroxyl radical to carbon atoms, the transfer of hydrogen to hydroxyl radical, isomerization, and bond cleavage, leading to low-weight inorganic compounds, such as ammonia, nitrogen gas, nitrous acid, nitric acid, and carbon(IV) oxide. The anionic form of NTO is more reactive toward interaction with the hydroxyl radical as compared with its neutral form. Calculated activation energies and high exergonicity of the studied process support the significant contribution of the hydroxyl radical to NTO mineralization in environment.

Fused Double Donor Design with a Cross-Conjugated Dibenzosilin for Dye-Sensitized Solar Cells

Dye-sensitized solar cells (DSCs) can provide a clean energy solution to growing energy demands. In order to have devices of high performance, sensitizers that are able to absorb in the near-infrared region (NIR) are needed. Stronger electron donors are needed for intramolecular charge-transfer sensitizers to access longer wavelength photons. Thus, two novel organic dyes with a cross-conjugated dibenzosilin double donor design are studied herein. The double donor delocalizes multiple filled orbitals across both amine donors due to the fused design that planarizes the donor as observed computationally, which improves intramolecular charge-transfer strength. The dyes are studied via density functional theory (DFT), optical spectroscopy, electrochemistry, and in DSC devices. The studies indicate that the dye design can reduce recombination losses, allowing for improved DSC device performances relative to a single arylamine donor. The reduction in recombination losses is attributed to the six alkyl chains that are incorporated into the donor, which offer good surface protection.

May the Number of Pregnancies Predict the Progression and the Outcome of Venous Disease Treatment?

Pregnancy is a well-known risk factor for venous insufficiency. However, even nulliparous women experience venous problems. Therefore, we aimed to assess the possible associations between the number of pregnancies, veins condition and treatment outcome in women with venous disease. The retrospective assessment concerned data of 297 women with diagnosed venous insufficiency. Based on their pregnancy history, the patients' records were divided into: nulliparous women (15.5%), those after 1-2 term pregnancies (57.9%) and those after ≥3 pregnancies (26.6%). The analysis concerned data from the diagnostics of the abdominal/pelvic and leg veins and the treatment results expressed as a symptoms/satisfaction score. Most of the nulliparous women developed venous disease due to mild anatomic abnormalities of the abdominal/pelvic veins. They responded to treatment (mostly unilateral embolization) very well. In the second group, the majority of the combined venous abnormalities responded to treatment with significant improvement, even after embolization alone, although nearly two-thirds still required further leg treatment. The third group was comprised of more advanced cases; nearly 40% of them with recurrence. In order to improve their condition, three-fourths of the cases required sequential treatment in both the pelvic and leg veins compartments. In conclusion, the number of pregnancies is a potent modifying factor in the pathogenesis of venous disease, especially in multiparous women. Together with abnormal venous anatomy, it may determine the treatment outcome.

Does the Treatment of Pelvic Venous Insufficiency Really Not Influence Lower Limb Venous Disease?

Pelvic venous insufficiency is a common problem in multiparous women. Besides burdensome symptoms, it correlates with the development of venous disease in the lower limbs. Therefore, the sequential treatment of abdominal/pelvic before leg veins could improve treatment effectiveness. The medical records of 243 patients with venous disease who were subjected to sequential treatment were analyzed retrospectively. The symptoms and patient satisfaction were assessed using dedicated questionnaires, both before and after treatment. Clinical effectiveness was verified using a Doppler scan, both before and after treatment. Among 243 analyzed cases, 195 underwent whole treatment; however, 48 women after embolization did not require further intervention. The total-symptom-score change (11.6 vs. 13.0, respectively) and the satisfaction score (1.6 vs. 1.5, respectively) did not differ between groups. After embolization, some patients, besides symptoms improvement, experienced reflux reduction and, hence, might avoid further intervention. A better explanation for this beneficial effect of the sequential/descending approach requires further studies.

Synthesis and Characterization of Novel Thienothiadiazole-Based D-π-A-π-D Fluorophores as Potential NIR Imaging Agents

As fluorescence bioimaging has increased in popularity, there have been numerous reports on designing organic fluorophores with desirable properties amenable to perform this task, specifically fluorophores with emission in the near-infrared II (NIR-II) region. One such strategy is to utilize the donor-π-acceptor-π-donor approach (D-π-A-π-D), as this allows for control of the photophysical properties of the resulting fluorophores through modulation of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energy levels. Herein, we illustrate the properties of thienothiadiazole (TTD) as an effective acceptor moiety in the design of NIR emissive fluorophores. TTD is a well-known electron-deficient species, but its use as an acceptor in D-π-A-π-D systems has not been extensively studied. We employed TTD as an acceptor unit in a series of two fluorophores and characterized the photophysical properties through experimental and computational studies. Both fluorophores exhibited emission maxima in the NIR-I that extends into the NIR-II. We also utilized electron paramagnetic resonance (EPR) spectroscopy to rationalize differences in the measured quantum yield values and demonstrated, to our knowledge, the first experimental evidence of radical species on a TTD-based small-molecule fluorophore. Encapsulation of the fluorophores using a surfactant formed polymeric nanoparticles, which were studied by photophysical and morphological techniques. The results of this work illustrate the potential of TTD as an acceptor in the design of NIR-II emissive fluorophores for fluorescence bioimaging applications.

The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds

CONTEXT

To apply the quantitative relationships "structure-endpoint" approach, the reliability of prediction is necessary but sometimes challenging to achieve. In this work, an attempt is made to accomplish the reliability of forecasts by creating a set of random partitions of data into training and validation sets, followed by constructing random models. A system of random models for a helpful approach should be self-consistent, giving a similar or at least comparable statistical quality of the predictions for models obtained using different splits of available data into training and validation sets.

METHOD

The carried out computer experiments aimed at obtaining blood-brain barrier permeation models showed that, in principle, can be used such an approach (the Monte Carlo optimization of the correlation weights for different molecular features) for the above purpose taking advantage of specific algorithms to optimize the modelling steps with applying of new statistical criteria such as the index of ideality of correlation (IIC) and the correlation intensity index (CII). The results so obtained are good and better than what was reported previously. The suggested approach to validation of models is non-identic to traditionally applied manners of the checking up models. The concept of validation can be used for arbitrary models (not only for models of the blood-brain barrier).

Investigating the Effects of Donors and Alkyne Spacer on the Properties of Donor-Acceptor-Donor Xanthene-Based Dyes

NIR dyes have become popular for many applications, including biosensing and imaging. For this reason, the molecular switch mechanism of the xanthene dyes makes them useful for in vivo detection and imaging of bioanalytes. Our group has been designing NIR xanthene-based dyes by the donor-acceptor-donor approach; however, the equilibrium between their opened and closed forms varies depending on the donors and spacer. We synthesized donor-acceptor-donor NIR xanthene-based dyes with an alkyne spacer via the Sonogashira coupling reaction to investigate the effects of the alkyne spacer and the donors on the maximum absorption wavelength and the molecular switching (ring opening) process of the dyes. We evaluated the strength and nature of the donors and the presence and absence of the alkyne spacer on the properties of the dyes. It was shown that the alkyne spacer extended the conjugation of the dyes, leading to absorption wavelengths of longer values compared with the dyes without the alkyne group. In addition, strong charge transfer donors shifted the absorption wavelength towards the NIR region, while donors with strong π-donation resulted in xanthene dyes with a smaller equilibrium constant. DFT/TDDFT calculations corroborated the experimental data in most of the cases. Dye 2 containing the N,N-dimethylaniline group gave contrary results and is being further investigated.

Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach

Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure-property-activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.

Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.

Serpentinization-Associated Mineral Catalysis of the Protometabolic Formose System

The formose reaction is a plausible prebiotic chemistry, famed for its production of sugars. In this work, we demonstrate that the Cannizzaro process is the dominant process in the formose reaction under many different conditions, thus necessitating a catalyst for the formose reaction under various environmental circumstances. The investigated formose reactions produce primarily organic acids associated with metabolism, a protometabolic system, and yield very little sugar left over. This is due to many of the acids forming from the degradation and Cannizaro reactions of many of the sugars produced during the formose reaction. We also show the heterogeneous Lewis-acid-based catalysis of the formose reaction by mineral systems associated with serpentinization. The minerals that showed catalytic activity include olivine, serpentinite, and calcium, and magnesium minerals including dolomite, calcite, and our Ca/Mg-chemical gardens. In addition, computational studies were performed for the first step of the formose reaction to investigate the reaction of formaldehyde, to either form methanol and formic acid under a Cannizzaro reaction or to react to form glycolaldehyde. Here, we postulate that serpentinization is therefore the startup process necessary to kick off a simple proto metabolic system-the formose protometabolic system.

CORAL: Model of Ecological Impact of Heavy Metals on Soils via the Study of Modification of Concentration of Biomolecules in Earthworms (Eisenia fetida)

The traditional application for quantitative structure-property/activity relationships (QSPRs/QSARs) in the fields of thermodynamics, toxicology or drug design is predicting the impact of molecular features using data on the measurable characteristics of substances. However, it is often necessary to evaluate the influence of various exposure conditions and environmental factors, besides the molecular structure. Different enzyme-driven processes lead to the accumulation of metal ions by the worms. Heavy metals are sequestered in these organisms without being released back into the soil. In this study, we propose a novel approach for modeling the absorption of heavy metals, such as mercury and cobalt by worms. The models are based on optimal descriptors calculated for the so-called quasi-SMILES, which incorporate strings of codes reflecting experimental conditions. We modeled the impact on the levels of proteins, hydrocarbons, and lipids in an earthworm's body caused by different combinations of concentrations of heavy metals and exposure time observed over two months of exposure with a measurement interval of 15 days.

Role of Molecular Singlet Oxygen in Photochemical Degradation of NTO: DFT Study

NTO (5-nitro-1,2,4-triazol-3-one), an energetic material used in military applications, may be released to the environment and dissolved in surface water and groundwater due to its good water solubility. Singlet oxygen is an important reactive oxygen species produced in the aquatic environment under sunlight irradiation. A detailed investigation of the possible mechanism for NTO decomposition in water induced by singlet oxygen as one of the pathways for NTO environmental degradation was performed by a computational study at PCM(Pauling)/M06-2X/6-311++G(d,p) level. Decomposition of NTO was found to be a multistep process that may begin with singlet oxygen attachment to the carbon atom of the C═N double bond. The formed intermediate undergoes cycle opening, and nitrogen gas, nitrous acid, and carbon (IV) oxide elimination. Isocyanic acid, arisen transiently, hydrolyzes into ammonia and carbon (IV) oxide. The obtained results show a significant increase in reactivity of the anionic form of NTO as compared to its neutral form. The calculated activation energies and high exothermicity of the studied processes support the contribution of singlet oxygen to NTO degradation into low-weight inorganic compounds in the environment.

Decomposition of 2,4,6-trinitrotoluene (TNT) and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO) by Fe13O13 nanoparticle: density functional theory study

To obtain more insight into the mechanisms of the decomposition of energetic compounds, we performed a computational study of the interaction of Fe₁₃O₁₃ nanoparticles with two energetic molecules such as 2,4,6-trinitrotoluene (TNT) and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO). The density functional theory using M06-2X, B3LYP, and BLYP density functionals was applied. We found that the reactivity of these molecules strongly depends on the place of adsorption (so-called top and bottom planes of Fe₁₃O₁₃). Namely, only the interaction with the bottom plane results in the thermodynamic characteristics of the decomposition that provide a medium reaction rate for the studied processes. Several pathways for such decomposition were found. One of them is the inter-complex oxygen transfer of nitro-group oxygen to Fe₁₃O₁₃. This pathway results in the formation of adsorbed nitroso compounds. The second pathway describes a more complex decomposition that includes the transfer of the nitro-group oxygen accompanied by the hydrogen transfer. In all cases, the interaction of energetic molecules with Fe₁₃O₁₃ nanoparticles takes place along with a barrier-less electron transfer from Fe₁₃O₁₃ to TNT or NTO species.

NTO Degradation by Nitroreductase: A DFT Study

NTO (5-nitro-1,2,4-triazol-3-one), an energetic material used in military applications, may be released to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of the possible mechanism for all steps of reduction of NTO by oxygen-insensitive nitroreductase, as one of the pathways for NTO environmental degradation, was performed by computational study at the PCM(Pauling)/M06-2X/6-311++G(d,p) level. Obtained results reveal an overall sequence for NTO transformation into ATO (5-amino-1,2,4-triazol-3-one) with the flavin mononucleotide (FMN) cofactor of nitroreductase. Reduction of the nitro group to the nitroso group and the nitroso group to the hydroxylamino group follow a similar mechanism that consists of the sequential electron and proton transfer from the flavin cofactor. The hydride transfer mechanism may contribute to reduction of the nitroso group by the anionic form of the reduced flavin cofactor. Reduction of 5-(hydroxylamino)-1,2,4-triazol-3-one by the neutral form of the reduced flavin is impossible, whereas reduction of the hydroxylamino group to the amino group occurs with the anionic form of the reduced cofactor by a mechanism involving an initial proton transfer from the hydroxonium ion followed by two electrons and one proton transfers from the flavin cofactor. Small activation energies and high exothermicity support the significant contribution of oxygen-insensitive nitroreductase and other enzymes, containing FMN as a cofactor, to NTO degradation in the environment.

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

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.

In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach

Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the 'independent action' generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through 'independent action' to induce cytotoxicity to E. coli instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion - toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Enhanced Perovskite Solar Cell Performance via 2-Amino-5-iodobenzoic Acid Passivation

The intrinsic stability issues of the perovskite materials threaten the efficiency and stability of the devices, and stability has become the main obstacle to industrial applications. Herein, the efficient and facile passivation strategy by 2-amino-5-iodobenzoic acid (AIBA) is proposed. The impact of AIBA on the properties of the perovskite films and device performance is systemically studied. The results show that the trap states are eliminated without affecting the crystal properties of the perovskite grains, leading to the enhanced performance and stability of the perovskite solar cells (PSCs). A high power conversion efficiency (PCE) of 20.23% and lower hysteresis index (HI) of 1.49‰ are achieved, which represent one of the most excellent PCE and HI values for the inverted PSCs based on MAPbI₃/[6,6]-Phenyl-C61-Butyric Acid Methyl Ester (PCBM) planar heterojunction structure. Moreover, the UV stability of the perovskite films and the thermal and moisture stability of the devices are also enhanced by the AIBA passivation. The PCE of the device with AIBA can maintain about 83.41% for 600 h (40 RH %) and 64.06% for 100 h (55-70 RH %) of its initial PCE value without any encapsulation, while the control device can maintain only about 72.91 and 45.59% of its initial PCE. Density functional theory calculations are performed to study the origins of enhanced performance. Interestingly, the results show that the surface states induced by AIBA can facilitate the photoexcited charge transfer dynamics and reduce the electron-hole recombination loss. The passivation method developed in this work provides an efficient way to enhance the stability and performance of inverted PSCs.

Green Chemistry in the Synthesis of Pharmaceuticals

The principles of green chemistry (GC) can be comprehensively implemented in green synthesis of pharmaceuticals by choosing no solvents or green solvents (preferably water), alternative reaction media, and consideration of one-pot synthesis, multicomponent reactions (MCRs), continuous processing, and process intensification approaches for atom economy and final waste reduction. The GC's execution in green synthesis can be performed using a holistic design of the active pharmaceutical ingredient's (API) life cycle, minimizing hazards and pollution, and capitalizing the resource efficiency in the synthesis technique. Thus, the presented review accounts for the comprehensive exploration of GC's principles and metrics, an appropriate implication of those ideas in each step of the reaction schemes, from raw material to an intermediate to the final product's synthesis, and the final execution of the synthesis into scalable industry-based production. For real-life examples, we have discussed the synthesis of a series of established generic pharmaceuticals, starting with the raw materials, and the intermediates of the corresponding pharmaceuticals. Researchers and industries have thoughtfully instigated a green synthesis process to control the atom economy and waste reduction to protect the environment. We have extensively discussed significant reactions relevant for green synthesis, one-pot cascade synthesis, MCRs, continuous processing, and process intensification, which may contribute to the future of green and sustainable synthesis of APIs.

Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (Mpro) through docking, molecular mechanic & dynamic, and ADMET profiling

Due to an outbreak of COVID-19, the number of research papers devoted to in-silico drug discovery of potential antiviral drugs is increasing every day exponentially. Still, there is no specific drug to prevent or treat this novel coronavirus (SARS-CoV-2) disease. Thus, the screening for a potential remedy presents a global challenge for scientists. Up to date over a hundred crystallographic structures of SARS-CoV-2 Mpro have been deposited to Protein Data Bank. With many known proteins, the demand for a reliable target has become higher than ever, so as the choice of an efficient computational methods. Therefore, in this study comparative methods have been used for receptor-based virtual screening, targeting 9 selected structures of viral Mpro. Reliability analyses followed by re-docking of the specific co-crystallized ligand provided the best reproductivity for structures with PDB ID 6LU7, 6Y2G and 6Y2F. The influence of crystallographic water on an outcome of a virtual screening against selected targets was also investigated. Once the most reliable targets were selected, the library of easy purchasable natural compounds were retrieved from the MolPort database (10,305 compounds) and docked against the selected Mpro proteins. To ensure the efficiency of the selected compounds, binding energies for top-15 hit ligands were calculated using Molecular Mechanics as well as their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were predicted. Based on predicted binding energies and toxicities, top-5 compounds were selected and subjected to Molecular Dynamics simulation and found to be stable in complex to act as possible inhibitors for SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Theoretical DFT Study on the Mechanisms of CO/CO2 Conversion in Chemical Looping Catalyzed by Calcium Ferrite

The CO/CO₂ conversion mechanism on the calcium ferrite (CFO) surface in chemical looping was explored by a computational study using the density functional theory approach. The CFO catalytic reaction pathway of 2CO + O₂ → 2CO₂ conversion has been elucidated. Our results show that the Fe center in CFO plays the key role as a catalyst in the CO/CO₂ conversion. Two energetically stable spin states of CFO, quintet and septet, serve as the effective catalysts. The presence of the triplet O₂ molecule caused the conversion of these two spin-state structures into each other along the catalytic reaction pathway. A double release of CO₂ was predicted following this reaction mechanism. The rate-determining step is the formation of the 2CO₂-CFO complex (P4) in the quintet state (19.0 kcal/mol). The predicted energy barriers for all the steps suggest that the proposed pathway is plausible.

Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549 cells

Cell death is critical to human health and is associated with a variety of medical conditions. Therefore, new controllers of cell death are needed for the treatment of diverse diseases. In particular, nanoparticles (NP) are now regularly used in various applications, including a variety of products and medicines. Gold nanoparticles (GNPs) are widely used in the medical field against A549 lung carcinoma cells. The present study is devoted to developing computational models of the cellular uptake potentials by A549 cells of gold nanoparticles (GNPs) under various conditions. Simplified molecular input-line entry system (SMILES) is an efficient tool to represent the molecular structure by a sequence of symbols. Quasi-SMILES represents an extended version of SMILES where symbols to denote physicochemical and/or biochemical conditions are added. In other words, the quasi-SMILES represents a biochemical (medical) phenomenon related to the whole matter (not only molecular structure). We developed models for the cellular utpake potential of gold nanoparticles (GNPs) in A549 [10-11 g Au/Cell] under various conditions based on quasi-SMILES using the Monte Carlo method. The statistical quality of these models is quite good.

Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions

The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al₂O₃, CuO, Fe₂O₃, Fe₃O₄, SiO₂, TiO₂, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.

Interaction of epoxy-based hydrogels and water: A molecular dynamics simulation study

Biomaterials play a crucial role in tissue engineering as a functional replacement, regenerative medicines, supportive scaffold for guided tissue growth, and drug delivery devices. The term biomaterial refers to metals, ceramics, and polymers account for the vast majority. In the case of polymers, hydrogels have emerged as active materials for an immense variety of applications. Epoxy-based hydrogels possess a unique network structure that enables very high levels of hydrophilicity and biocompatibility. Hydrogel such as Medipacs Epoxy Polymers (MEPs) models were constructed to understand water's behavior at the water/hydrogel interface and hydrogel network. We computed the Gibbs dividing surface (GDS) to define the MEP/water interface, and all the physicochemical properties were computed based on GDS. We calculated the radial distribution function (RDF), the 2D surface roughness of the immersed MEPs. RDF analysis confirmed that the first hydration shell is at a distance of 1.86 Å, and most of the water molecules are near the hydroxyl group of the MEPs network. Hydrogen bonds (H-bonds) analysis was performed, and the observation suggested that the disruption of the H-bonds between MEP chains leads to an increase in the polymer matrix's void spaces. These void spaces are filled with diffused water molecules, leading to swelling of the MEP hydrogel. The swelling parameter was estimated from the fitted curve of the yz-lattice of the simulation cell. The MEP/water interface simulation results provide insightful information regarding the design strategy of epoxy-based hydrogel and other hydrogels vital for biomedical applications.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).

Single site Fe on the (110) surface of γ-Al2O3: insights from a DFT study including the periodic boundary approach

Examination of the stable (110) surface of γ-alumina reveals that there are three different types of sites available to host a single Fe atom. With the carefully calibrated density functional approach (M12-L/SV), three types of Fe single sites on the (110) surface of γ-alumina have been investigated under the periodic boundary conditions. The most stable Fe replacement site on the (110) surface of γ-alumina has been found to be represented by the tri-coordinated Fe_I position with the quartet spin state. The replacement of Al by Fe atoms at the Al site leads to charge redistributions of the neighboring O atoms. However, sublayer charge distribution is less affected. A significant contribution of p orbitals of the O atoms in the surface phase to the LUMO has been found for the tri-coordinated Fe_I substitution on the (110) surface. The corresponding oxygen atoms (O_A and O_A1) have been activated due to the existence of Fe_I in their neighborhood. The loosened neighboring Al_III-O_A bonds match this activation. This activation of O suggests the existence of an important source of the reactive O during the Fe catalytic oxidation of CO processes.

The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models

The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.

Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies

The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er₂O₃, Gd₂O₃, CeO₂, Co₂O₃, Mn₂O₃, Co₃O₄, Fe₃O₄/WO₃ (in descending order). The computed EC₅₀ values from experimental data suggested that Er₂O₃ and Gd₂O₃ were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1^st and 2^nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.

Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery.

The Analysis of Selected Morphological and Hemodynamic Parameters of the Venous System and Their Presumable Impact on the Risk of Recurrence after Varicose Vein Treatment

INTRODUCTION

The current treatment of venous disease is focused on reflux elimination in main venous trunks, especially in the saphenous vein. However, a high recurrence rate, independent of the method of treatment, suggests that the reason of low effectiveness may be due to a strategy focused on symptoms, without considering their origin.

METHOD

The aim of study was the comparison of retrospective data from 535 women with venous disease, either after treatment (n = 183) or not treated before (n = 352). The analysis concerned clinical symptoms and the results of the extended diagnostics, including the examination of the lower limb, pelvic and abdominal veins either using duplex-doppler ultrasound as well as venography with computed tomography or magnetic resonance.

RESULTS

The comparison of selected venous system parameters revealed more advanced disease progression in previously treated patients, compared to non-treated individuals (e.g., ipsi- or bilateral incompetence of sapheno-phemoral junction-29.5% vs. 20.4%, at P < 0.05 and 13.6% vs. 7.7% at P < 0.05, respectively). This difference could be explained by post-treatment alterations in the venous system, an older age and the higher number of pregnancies in the recurrence group. However, both groups did not differ in regards to the symptoms of pelvic venous insufficiency or the frequency of relevant variants/abnormalities in venous system.

CONCLUSIONS

Based on the aforementioned findings, we postulate the revision of treatment strategy, which should consider abdominal and pelvic veins as the source of reflux in many female subjects.

Evaluating Donor Effects in Isoindigo-Based Small Molecular Fluorophores

Small molecular organic fluorophores have garnered significant interest because of their indispensable use in fluorescence imaging (FI) and optoelectronic devices. Herein, we designed triphenylamine (TPA)-capped donor-acceptor-donor (D-A-D)-based fluorophores having a variation at the heterocyclic donor (D) units, 3,4-ethylenedioxythiophene (EDOT), furan (FURAN), thiophene (THIO), and 1-methyl-1H-pyrrole (MePyr), with isoindigo as the core electron acceptor (A) unit. Synthesis of these fluorophores (II-X-TPA) resulted in four symmetrical dye molecules: II-EDOT-TPA, II-FURAN-TPA, II-THIO-TPA, and II-MePyr-TPA, where TPA functioned as a terminal unit and a secondary electron donor group. Photophysical, electrochemical, and computational analyses were conducted to investigate the effect of heterocyclic donor units on the II-X-TPA derivatives. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations provided insightful features of structural and electronic properties of each fluorophore and correlated well with experimental observations. Electron density distribution maps, overlapping frontier molecular orbital diagrams, and highest occupied molecular orbital (HOMO) to lowest unoccupied molecular orbital (LUMO) electron transfer indicated intramolecular charge transfer (ICT). Theoretical studies confirmed the experimental HOMO energy trend and demonstrated its crucial importance in understanding each heterocycle's donor ability. Stokes shifts of up to ∼178 nm were observed, whereas absorptions and emissions were shifted deeper into the NIR region, resulting from ICT. Results suggest that this isoindigo fluorophore series has potential as a molecular scaffold for the development of efficient FI agents. The studied fluorophores can be further tuned with different donor fragments to enhance the ICT and facilitate in shifting the optical properties further into the NIR region.

Is intraspecies QSTR model answer to toxicity data gap filling: Ecotoxicity modeling of chemicals to avian species

Interspecies model represents an established approach for the response data gap filling for regulatory agencies and researchers. We propose a novel approach of intraspecies modeling within the animals of the same species, instead of animals from different species. The proposed intraspecies model is capable of more precise extrapolation of data than the interspecies model, as animals under the same species share a similar mechanism of action (MOA) and target sites for the response. Along with the advantage of better prediction over the interspecies model, the intraspecies model has all the significant features like recognition of MOA, species-specific toxicity, reduction of animal experimentation, and money and time. To establish and test the intraspecies modeling approach, we have modeled ecotoxicity of organic chemicals to three avian species: Anas platyrhynchos, Colinus virginianus, and Phasianus colchicus. The intraspecies models offer to identify the mechanistic interpretation of the ecotoxicity of the studied chemicals along with the toxicity data gap filling. The success of the intraspecies modeling relies on connecting the missing dots of toxicity for the regulatory purposes, especially when there is a scarcity of ecotoxicity experimental data and in silico models for avian species.

SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model

The main protease (M^pro) of SARS-associated coronavirus (SARS-CoV) had caused a high rate of mortality in 2003. Current events (2019-2020) substantiate important challenges for society due to coronaviruses. Consequently, advancing models for the antiviral activity of therapeutic agents is a necessary component of the fast development of treatment for the virus. An analogy between anti-SARS agents suggested in 2017 and anti-coronavirus COVID-19 agents are quite probable. Quantitative structure-activity relationships for SARS-CoV are developed and proposed in this study. The statistical quality of these models is quite good. Mechanistic interpretation of developed models is based on the statistical and probability quality of molecular alerts extracted from SMILES. The novel, designed structures of molecules able to possess anti-SARS activities are suggested. For the final assessment of the designed molecules inhibitory potential, developed from the obtained QSAR model, molecular docking studies were applied. Results obtained from molecular docking studies were in a good correlation with the results obtained from QSAR modeling.

From Animal to Human: Interspecies Analysis Provides a Novel Way of Ascertaining and Fighting COVID-19

There is a strong risk of mutations in the genome sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in animals. Consequently, a possibility of zoonotic transfer of a much stronger form of the present virus from animal to human is also very feasible in the near future. Thus, recent incidents of SARS-CoV-2 infection in animals need to be studied very carefully to protect against any future transmissions. Interspecies analysis from animal to human or vice versa is the tool of choice at the present time for understanding zoonotic transfer and improving/accelerated drug discovery for COVID-19.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.