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).

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).

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.

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.

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.

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

The main protease (Mpro) 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.

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.

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 1st and 2nd 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.

How the CORAL software can be used to select compounds for efficient treatment of neurodegenerative diseases?

Recommendations on the efficient application of CORAL software (http://www.insilico.eu/coral) to establish quantitative structure-property/activity relationships (QSPRs/QSARs) are provided. The predictive potential of the approach has been demonstrated for QSAR models developed for inhibitor concentrations (negative decimal logarithm of IC50) of derivatives of N-methyl-d-aspartate (NMDA) receptor, leucine-rich repeat kinase 2 (LRRK2), and tropomyosin receptor kinase A (TrkA). The above three protein targets are related to various neurodegenerative diseases such as Alzheimer's and Parkinson's. Each model was checked using several splits of the data for the training and the validation sets. The index of ideality of correlation (IIC) represents a tool to improve the predictive potential for an arbitrary model. However, the use of the IIC should be carried out according to rules, described in this work.

A density functional theory investigation of degradation of Nitroguanidine in the photoactivated triplet state.

It is well known that nitroguanidine (NQ) undergoes photodegradation when exposed to UV-radiation. However, the mechanism of NQ photolysis is not fully understood. Earlier investigations have shown that nitrocompounds undergo to their triplet state population through crossing of electronic singlet and triplet excited state potential energy surfaces due to the nitrogroup rotation and nonplanarity under electronic excitation. Therefore, it is expected that under electronic excitation, the presence of nitrogroup in NQ would also lead to the population of electronic lowest energy triplet state. To shed a light on the degradation of NQ in alkaline solution under electronic excitation, we performed a detailed investigation of a possible degradation mechanism at the IEFPCM/B3LYP/6-311++G(d,p) level in the electronic lowest energy triplet state. We found that degradation ability of NQ in the electronic triplet state would be significantly larger than in the electronic ground singlet state. It was revealed that the photodecomposition of nitroguanidine might occur through several pathways involving N-N and C-N bond ruptures, nitrite elimination, and hydroxide ion attachment. Nitrogen of nitrogroup would be released in the form of nitrite and nitrogen (I) oxide. Computationally predicted intermediates and products of nitroguanidine photolysis such as nitrite, hydroxyguanidine, cyanamide, and urea correspond to experimentally observed species.

Role of Singlet Oxygen in the Degradation of Selected Insensitive Munitions Compounds: A Comprehensive, Quantum Chemical Investigation.

DNAN (2,4-dinitroanisole), NTO (3-nitro-1,2,4-triazol-5-one), and NQ (nitroguanidine) are important energetic materials used in military applications. They may find their way to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of possible mechanisms for reactions of the nitrocompounds with singlet oxygen, one of the potential methods for their degradation, was performed by computational study using the PCM(Pauling)/M06-2X/6-311++G(d,p) approach. Obtained results suggest that reactivity of the investigated munitions compounds toward singlet oxygen follows the order: DNAN > NTO(anion) > NQ ≫ NTO. DNAN is involved in [4 + 2]-addition with oxygen, and further formation of diepoxide or epoxyketone through diradical intermediates have been predicted. The NTO may undergo intramolecular rearrangement with formation of peroxide compound or nitrite radical elimination, and NQ may be transformed into urea.

"Ideal correlations" for biological activity of peptides.

Sequences of one-symbol abbreviations of amino acids are applied as the basis to build up predictive model of Angiotensin converting enzyme (ACE) inhibitory activity of dipeptides and antibacterial activity of group of polypeptides. The developed models are one-variable correlations between biological activity and descriptors calculated with so-called correlation weights of amino acids. The numerical data on the correlation weights are obtained by the Monte Carlo method. The Index of Ideality of Correlation (IIC) is a mathematical function of (i) the determination coefficient; and (ii) sums of positive and negative values of "observed minus predicted" endpoints values. The obtained results confirm that IIC can be applied to improve predictive potential of models for ACE inhibitor activity of dipeptides and antibacterial activity of polypeptides.

Adsorption of nitrogen-containing compounds on hydroxylated α-quartz surfaces.

Adsorption energies of various nitrogen-containing compounds (specifically, 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), 2,4-dinitroanisole (DNAn), and 3-nitro-1,2,4-triazole-5-one (NTO)) on the hydroxylated (001) and (100) α-quartz surfaces are computed. Different density functionals are utilized and both periodic as well as cluster approaches are applied. From the adsorption energies, partition coefficients on the considered α-quartz surfaces are derived. While TNT and DNT are preferably adsorbed on the (001) surface of α-quartz, NTO is rather located on both α-quartz surfaces.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential.

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique.

Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.

Helical Multi-walled Carbon Nanotubes as an Efficient Material for the Dispersive Solid-Phase Extraction of Low and High Molecular Weight Polycyclic Aromatic Hydrocarbons from Water Samples: Theoretical Study.

The differences in effectiveness of multi-walled carbon nanotubes (MWCNTs) as the dispersive solid-phase extraction (dSPE) sorbent for the selective extraction of polycyclic aromatic hydrocarbons (PAHs) were explained on the basis of theoretical study. It was observed that for low molecular weight PAHs, the recoveries using non-helical and helical MWCNTs were similar. In contrary, for PAHs containing five or more aromatic rings, the extraction efficiency was higher using HMWCNTs than for non-helical ones. Principle component analysis (PCA) as well as providing structural parameters and interaction energies for adsorption processes (PAH + CNT → PAH-CNT) have been used for this purpose. All the PAH + CNT → PAH-CNT adsorption processes considered were found to be thermodynamically favorable. However, the adsorption energies (Eads) for PAHs and the helical carbon nanotube surface estimated for the B(a)P-HCNT and I(1,2,3-cd)P-HCNT are substantially less negative than those observed for PAH molecules interacting with the non-helical CNT. Namely, the Eads calculated in simulated aqueous environment for the B(a)P-MWCNT(6,2) and I(1,2,3-cd)P-MWCNT(6,2) were respectively - 43.32 and - 59.98 kcal/mol, while values of only - 7.75 kcal/mol (B(a)P-HCNT) and - 9.13 kcal/mol (I(1,2,3-cd)P-HCNT) were found for the corresponding PAH-HCNT systems. Therefore, we conclude that the replacement of MWCNTs with HCNTs leads to PAH-HCNT systems in which the interaction energies are much smaller than those estimated for the corresponding PAH-MWCNT systems. HMWCNTs are therefore recommended as the dSPE sorbent phase for the extraction of both low and high molecular weight PAHs from water samples.

Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES.

The purpose of this study was the estimation of ability of the so-called optimal descriptors calculated to be a tool to predict the antimicrobial activity of large pool of peptides. Traditional simplified molecular input-line entry system (SMILES) is an efficient tool to represent the molecular structure of different compounds. Quasi-SMILES represents an extension of traditional SMILES. This approach provides the possibility to involve different eclectic conditions related to analyzed endpoint in the modelling process. In addition, the quasi-SMILES can be used to represent structure of peptides via abbreviations of corresponding amino acids. In this study, quasi-SMILES represents sequences of amino acids in peptides that were tested as the basis to predict antimicrobial activity of 1581 peptides. Predictive potential of binary classification for antimicrobial activity for different splits is quite good when it comes to the training, invisible training, calibration, and validation sets. For the external validation sets, the statistical criteria are ranged: (i) sensitivity 0.82-097; (ii) specificity 0.88-0.99; (iii) accuracy 0.87-0.98; and (iv) Matthews correlation coefficient 0.73-0.97. The suggested optimal descriptors calculated with data on composition of amino acids in peptides can be a tool to predict antimicrobial activity of peptides.

Interactions of Substituted Nitroaromatics with Model Graphene Systems: Applicability of Hammett Substituent Constants To Predict Binding Energies.

Applicability of Hammett parameters (σ m and σ p ) was tested in extended π-systems in gas phase. Three different model graphene systems, viz. 5,5-graphene (GR), 3-B-5,5-graphene (3BGR), and 3-N-5,5-graphene (3NGR), were designed as extended π-systems, and interactions of various nitrobenzene derivatives (mainly m- and p-substituted together with some multiple substitutions) on such platforms were monitored using density functional theory (M06/cc-pVDZ, M06/cc-pVTZ, M06/sp-aug-cc-pVTZ) and Møller-Plesset second-order perturbation (MP2/cc-pV-DZ) theory. Offset face to face (OSFF) stackings were found to be the favored orientations, and reasonable correlations were found between binding energies (ΔE B) and the ∑|σ m | values of the substituted nitrobenzenes. It was proposed previously that |σ m | contains information about the substituents' polarizability and controls electrostatic and dispersion interactions. The combination of ∑|σ m | and molar refractivity (as ∑M r) or change in polarizability (Δα: with respect to benzene) of nitrobenzene derivatives generated statistically significant correlation with respect to ΔE B, thereby supporting the hypothesis related to the validity of |σ m | correlations. The |σ p | parameters also maintain similar correlations for the various p-substituted nitrobenzene derivatives together with several multiply-substituted nitrobenzene derivatives. The correlation properties in such cases are similar to the |σ m | cases, and the energy partition analysis for both the situations reveled importance of electrostatic and dispersion contributions in such interactions. The applicability of Hammett parameters was observed previously on the restricted parallel face to face orientation of benzene···substituted benzene systems, and the present results show that such an idea could be used to predict ΔE B values in OSFF orientations, if the scaffolds are designed in such a way that substituted benzene systems cannot escape their π-clouds.

Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms.

The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles' structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure-activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner-Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R²bagging = 0.90 and external predictivity Q²EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr2O3, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action.

CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2).

Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.