Direct observation of split-mode exciton-polaritons in a single MoS2 nanotube

A single nanotube synthesized from a transition metal dichalcogenide (TMDC) exhibits strong exciton resonances and, in addition, can support optical whispering gallery modes. This combination is promising for observing exciton-polaritons without an external cavity. However, traditional energy-momentum-resolved detection methods are unsuitable for this tiny object. Instead, we propose to use split optical modes in a twisted nanotube with the flattened cross-section, where a gradually decreasing gap between the opposite walls leads to a change in mode energy, similar to the effect of the barrier width on the eigenenergies in the double-well potential. Using micro-reflectance spectroscopy, we investigated the rich pattern of polariton branches in single MoS2 tubes with both variable and constant gaps. Observed Rabi splitting in the 40-60 meV range is comparable to that for a MoS2 monolayer in a microcavity. Our results, based on the polariton dispersion measurements and polariton dynamics analysis, present a single TMDC nanotube as a perfect polaritonic structure for nanophotonics.

Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles

Simplified molecular input-line entry systems (SMILES) are the representation of the molecular structure that can be used to establish quantitative structure-property/activity relationships (QSPRs/QSARs) for various endpoints expressed as mathematical functions of the molecular architecture. Quasi-SMILES is extending the traditional SMILES by means of additional symbols that reflect experimental conditions. Using the quasi-SMILES models of toxicity to tadpoles gives the possibility to build up models by taking into account the time of exposure. Toxic effects of experimental situations expressed via 188 quasi-SMILES (the negative logarithm of molar concentrations which lead to lethal 50% tadpoles effected during 12 h, 24 h, 48 h, 72 h, and 96 h) were modelled with good results (the average determination coefficient for the validation sets is about 0.97). In this way, we developed new models for this amphibian endpoint, which is poorly studied.

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r²_VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Carcinogenicity prediction using the index of ideality of correlation

Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.

The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method

Perhaps there is some similarity between the coronavirus of 2017 and the COVID-19. Consequently, a predictive model for the antiviral activity for the Middle East respiratory syndrome coronavirus (MERS-CoV, 2017) could be useful for designing the strategy and tactics in the struggle with coronaviruses in general and with COVID 19 in particular. Quantitative structure-activity relationships (QSARs) of inhibitory activity to MERS-CoV were developed. The index of ideality of correlation was applied to build up these models for the antiviral activity. The statistical quality of the best model is quite good (r² = 0.84). A mechanistic interpretation of these models based on the molecular features with strong positive (i.e. promoters for endpoint increase) and strong negative (i.e. promoters for endpoint decrease) influence on the inhibitory activity is suggested. A collection of possible biologically active compounds, constructed using data on the above molecular features which are statistically reliable promoters of increase or decrease of the activity, is presented.

The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds

The hydrolysis of organic chemicals such as pesticides, pollutants, or drugs can affect the fate and behaviour of environmental contaminants, so it is of interest to evaluate the stability of substances in water for various purposes. For the registration of organic compounds in Europe, information on hydrolysis must be presented. However, the experimental measurements of all chemicals would require enormous resources, and computational models may become attractive. Applying the CORAL software (http://www.insilico.eu/coral) quantitative structure-property relationships (QSPRs) were built up to model hydrolysis. The 2D-optimal descriptor is calculated with so-called correlation weights for attributes of simplified molecular input-line entry systems (SMILES). The correlation weights are obtained as results of the special Monte Carlo optimization. The nature of (five- and six-member) rings is an important component of this approach. Another important component is the atom pair proportions for nitrogen, oxygen, and sulphur. The statistical quality of the best model is: n = 44, r² = 0.74 (training set); n = 14, r² = 0.75 (calibration set); and n = 12, r² = 0.80 (validation set).

EFSA's OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach

Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). The aim of the present study was the building and estimation of models for inhalation toxicity as No Observed Adverse Effect Concentration (NOAEC) based on the OECD guidelines 413. Three random distributions into the training set and validation set were examined. In practice, a structured training set that contains active training set, passive training set and calibration set is used as the training set. The statistical characteristics of the best model for negative logarithm of NOAEC (pNOAEC) are for training set n = 108, average r ² = 0.52 + 0.62 + 0.76/3 = 0.63 and for validation set n = 35, r ² = 0.73.

QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC₅₀) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r² = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r² = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r² = 0.74 and 0.75 for the CORAL model).

Strongly Confined Excitons in GaN/AlN Nanostructures with Atomically Thin GaN Layers for Efficient Light Emission in Deep-Ultraviolet

Fascinating optical properties governed by extremely confined excitons have been so far observed in 2D crystals like monolayers of transition metal dichalcogenides. These materials, however, are limited for production by epitaxial methods. Besides, they are not suitable for the development of optoelectronics for the challenging deep-ultraviolet spectral range. Here, we present a single monolayer of GaN in AlN as a heterostructure fabricated by molecular beam epitaxy, which provides extreme 2D confinement of excitons, being ideally suited for light generation in the deep-ultraviolet. Optical studies in the samples, supplemented by a group-theory analysis and first-principle calculations, make evident a giant enhancement of the splitting between the dark and bright excitons due to short-range electron-hole exchange interaction that is a fingerprint of the strongly confined excitons. The practical significance of our results is in the observation of the internal quantum yield of the room-temperature excitonic emission as high as ∼75% at 235 nm.

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure-property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The 'ideal correlation' is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.

Localization and transient emission properties in InGaN/GaN quantum wells of different polarities within core-shell nanorods

Transient photoluminescence (PL) characteristics and localization phenomena in InGaN/GaN core-shell nanorods (NRs) were investigated from 6 K up to 285 K. The NRs exhibit three well-defined PL bands in the near-UV, blue, and green range ascribed to the emission of quantum well (QW) areas situated at the (1.00) sidewalls, (10.1) top facets, and (00.1) tip, respectively. At low temperature, time-resolved PL shows a fast decay time of about 0.5 ns for the semi- and non-polar QWs, while the polar QWs exhibit at least a twice-longer time. Rapid delocalization of carriers above 50 K indicates shallow potential fluctuations in the QWs. At room temperature, the characteristic fast PL decay time of the three QW bands stabilizes around 300 ps. The slow decaying PL components have different characteristic decay times that are explained by additional localization at basal stacking faults (BSFs), taking into account the quantum confined Stark effect. In addition, narrow excitonic luminescence lines are observed in the BSF-enriched polar QWs, providing direct evidence of the impact of the BSF/QW crossings on the optical properties of the NRs. A PL rise time of about 100 ps does not show any deviation between bands. These findings are suggestive of similar transport mechanisms in temperature equilibrium without inter-facet transport between different QWs. We believe that predictable transient characteristics can play a key role in creating uniform NR ensembles for device applications.

InAs/AlGaAs quantum dots for single-photon emission in a red spectral range

We report on comparative optical studies of InAs/Al_0.44Ga_0.56As quantum dots (QDs) grown by molecular beam epitaxy either with or without a thin GaAs interlayer inserted between the AlGaAs barrier and InAs QDs. Emission properties of individual QDs are investigated by micro-photoluminescence spectroscopy using 500-nm-size etched cylindric mesa structures. The single-photon statistics of the QDs of both types, emitting in the red spectral range between 636 and 750 nm, is confirmed by the measurements of the second-order correlation function. A negligibly small exciton fine structure splitting is detected in the majority of the QDs grown with the GaAs interlayer that implies the possibility of generating pairs of entangled photons with high entanglement fidelity.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach

The solubility of gases in various polymers plays an important role for the design of new polymeric materials. Quantitative structure-property relationship (QSPR) models were designed to predict the solubility of gases such as CO2 and N2 in polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl acetate (PVA) and poly (butylene succinate) (PBS) at different temperatures and pressures by using quasi-SMILES codes. The dataset of 315 systems was split randomly into training, calibration and validation sets; random split 1 led to 214 training (r(2) = 0.870 and RMSE = 0.019), 51 calibration (r(2) = 0.858 and RMSE = 0.020) and 50 validation (r(2) = 0.869 and RMSE = 0.017) sets. The suggested approach based on the quasi-SMILES, which are analogues of the traditional SMILES gives reasonable good predictions for solubility of CO2 and N2 in different polymers. The described methodology is universal for situations where the aim is to predict the response of an eclectic system upon a variety of physicochemical and/or biochemical conditions.

A quasi-QSPR modelling for the photocatalytic decolourization rate constants and cellular viability (CV%) of nanoparticles by CORAL

Most quantitative structure-property/activity relationships (QSPRs/QSARs) predict various endpoints related to organic compounds. Gradually, the variety of organic compounds has been extended to inorganic, organometallic compounds and polymers. However, the so-called molecular descriptors cannot be defined for super-complex substances such as different nanomaterials and peptides, since there is no simple and clear representation of their molecular structure. Some possible ways to define approaches for a predictive model in the case of super-complex substances are discussed. The basic idea of the approach is to change the traditionally used paradigm 'the endpoint is a mathematical function of the molecular structure' with another paradigm 'the endpoint is a mathematical function of available eclectic information'. The eclectic data can be (i) conditions of a synthesis, (ii) technological attributes, (iii) size of nanoparticles, (iv) concentration, (v) attributes related to cell membranes, and so on. Two examples of quasi-QSPR/QSAR analyses are presented and discussed. These are (i) photocatalytic decolourization rate constants (DRC) (10(-5)/s) of different nanopowders; and (ii) the cellular viability under the effect of nano-SiO(2).

Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach

The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.

CORAL: building up the model for bioconcentration factor and defining it's applicability domain

CORAL (CORrelation And Logic) software can be used to build up the quantitative structure--property/activity relationships (QSPR/QSAR) with optimal descriptors calculated with the simplified molecular input line entry system (SMILES). We used CORAL to evaluate the applicability domain of the QSAR models, taking a model of bioconcentration factor (logBCF) as example. This model's based on a large training set of more than 1000 chemicals. To improve the model is predictivity and reliability on new compounds, we introduced a new function, which uses the Delta(obs) = logBCF(expr)--logBCF(calc) of the predictions on the chemicals in the training set. With this approach, outliers are eliminated from the phase of training. This proved useful and increased the model's predictivity.

Optically enhanced emission of localized excitons in InxGa1-xN films by coupling to plasmons in a gold nanoparticle

We report on strong site-selective enhancement of the emission of localized excitons in an InxGa1-xN film, induced by resonant coupling to a single plasmon confined in a gold nanoparticle. The particle was attached to an atomic-force-microscope probe and placed at the near-field distance of the surface. The observation is explained by the enhancement of the spontaneous emission recombination rate of the excitons due to the local increase in the photonic mode density near the metal particle. The interpretation is consistent with the intensity increase and lifetime shortening of the emission observed in the same film with deposited Au clusters. We show that the nanoscale roughness of the film is an important prerequisite of the efficient plasmonic enhancement.

Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain

The additive SMILES-based optimal descriptors have been used for modelling the bee toxicity. The influence of relative prevalence of the SMILES attributes in a training and test sets to the models for bee toxicity has been analysed. Avoiding the use of rare attributes improves statistical characteristics of the model on the external test set. The possibility of using the probability of the presence of SMILES attributes in training and test sets for rational definition of the applicability domain is discussed.

Resonant light delay in GaN with ballistic and diffusive propagation

We report on a strong delay in light propagation through bulk GaN, detected by time-of-flight spectroscopy. The delay increases resonantly as the photon energy approaches the energy of a neutral-donor bound exciton (BX), resulting in a velocity of light as low as 2100 km/s. In the close vicinity of the BX resonance, the transmitted light contains both ballistic and diffusive components. This phenomenon is quantitatively explained in terms of optical dispersion in a medium where resonant light scattering by the BX resonance takes place in addition to the polariton propagation.

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

QSPR modeling the aqueous solubility of alcohols by optimization of correlation weights of local graph invariants

Optimization of correlation weights of local graph invariants is an approach to model molecular properties and/or activities of chemical or/and biological interest. The essence of the approach may be described by means of three main steps: first, a descriptor which is a function of the weights of local graph invariants must be defined by the suitable choice among the different possibilities from the pool of molecular descriptors; second, correlation weights values which produce as large as possible correlation coefficient value between the selected property values and the descriptor data under consideration are calculated by Monte Carlo optimization procedure (the correlation coefficient is used as the quality objective function); third, a relationship such as property = C0 + C1 descriptor has to be calculated and validated with structures of some training set resorting to the standard least square method. We obtain quite satisfactory results using this calculation procedure to model the aqueous solubility of alcohols whose statistical characteristics are: n = 30, r = 0.9843, s = 0.176, F = 870 (Training Set); n = 33, r = 0.9965, s = 0.0902, F = 4456 (Test Set); n = 63, r = 0.9931, s = 0.121, F = 4379 (complete set of alcohol molecules).