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.

CORAL: QSAR models for acute toxicity in fathead minnow (Pimephales promelas).

CORrelation And Logic (CORAL) is a software that generates quantitative structure activity relationships (QSAR) for different endpoints. This study is dedicated to the QSAR analysis of acute toxicity in Fathead minnow (Pimephales promelas). Statistical quality for the external test set is a complex function of the split (into training and test subsets), the number of epochs of the Monte Carlo optimization, and the threshold that is a criterion for dividing the correlation weights into two classes rare (blocked) and not rare (active). Computational experiments with three random splits (data on 568 compounds) indicated that this approach can satisfactorily predict the desired endpoint (the negative decimal logarithm of the 50% lethal concentration, in mmol/L, pLC50). The average correlation coefficients (r2) are 0.675 ± 0.0053, 0.824 ± 0.0242, 0.787 ± 0.0101 for subtraining, calibration, and test set, respectively. The average standard errors of estimation (s) are 0.837 ± 0.021, 0.555 ± 0.047, 0.606 ± 0.049 for subtraining, calibration, and test set, respectively. The CORAL software together with three random splits into subtraining, calibration, and test sets can be downloaded on the Internet (http://www.insilico.eu/coral/).

CORAL: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical.

The rate constants (K(OH)) of reactions between 78 organic aromatic pollutants and hydroxyl radical were examined. Simplified molecular input line entry system was used as representation of the molecular structure of the pollutants. Quantitative structure-property relationships was developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the subtraining, calibration, and test sets. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r(2)) for the test sets of the four random splits are 0.75, 0.91, 0.84, and 0.80. Using the Monte Carlo method CORAL software generated the optimal descriptors for one-variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The current data were compared to previous results and discussed.

Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential.

Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model's predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r2 = 0.9468, and RMSE = 1.26 kJ/mol.

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.

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 (r2VAL = 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.

CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats.

For six random splits, one-variable models of rat toxicity (minus decimal logarithm of the 50% lethal dose [pLD50], oral exposure) have been calculated with CORAL software (http://www.insilico.eu/coral/). The total number of considered compounds is 689. New additional global attributes of the simplified molecular input line entry system (SMILES) have been examined for improvement of the optimal SMILES-based descriptors. These global SMILES attributes are representing the presence of some chemical elements and different kinds of chemical bonds (double, triple, and stereochemical). The "classic" scheme of building up quantitative structure-property/activity relationships and the balance of correlations (BC) with the ideal slopes were compared. For all six random splits, best prediction takes place if the aforementioned BC along with the global SMILES attributes are included in the modeling process. The average statistical characteristics for the external test set are the following: n = 119 ± 6.4, R(2) = 0.7371 ± 0.013, and root mean square error = 0.360 ± 0.037.

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.

QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors.

Quantitative structure-activity relationships (QSAR) for toxicity toward rats (pLD50) have been built by means of optimal descriptors. Comparison of the optimal descriptors calculated using the International Chemical Identifier (InChI) with the optimal descriptors calculated using the simplified molecular input line entry system (SMILES) has shown that the InChI-based models give more accurate prediction for the above-mentioned toxicity of organometallic compounds. These models were obtained by means of the balance of correlation: one subset of the training set (subtraining set) plays role of the training; the second subset (calibration set) plays role of the preliminary check of the models. It has been shown that the balance of correlations is a more robust predictor for the toxicity than the classic scheme (training set-test set: without the calibration set). Three splits into the subtraining set, calibration set, and test set were examined.

QSAR modelling of the toxicity to Tetrahymena pyriformis by balance of correlations.

Balance of correlations is an approach to build up quantitative structure-property/activity relationships (QSPR/QSAR). This approach is based on a split into the subtraining, calibration and test sets instead of classic split into training and test sets. The function of the calibration set is the preliminary check up of the model. In other words, the calibration set is like a preliminary test set. Computational experiments (with the Monte Carlo method) have shown that the statistical characteristics of the prediction for the toxicity to Tetrahymena pyriformis (the 50% growth inhibition concentration, IGC(50)) based on the balance of correlations are better than the statistical characteristics of the prediction based on the classic scheme.

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).

QSAR modelling of carcinogenicity by balance of correlations.

Optimal descriptors based on the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity. Carcinogenicity of 401 compounds has been modeled by means of balance of correlations for the training (n = 170) and calibration (n = 170) sets. The obtained models were evaluated with an external test set (n = 61). Comparison of models based on the balance of correlations and models which were obtained on the basis of the total training set (i.e., both training and calibration sets as the united training set) has shown that the balance of correlations improves the statistical quality for the external test set.

Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness.

The quantitative structure - activity relationships (QSARs) for sweetness value (log S) were built with a dataset of 315 molecules; following a novel criterion of 'Index of Ideality of Correlation(IIC)' This criterion of IIC is available in the latest version of the CORAL software (www.insilico.eu/coral). The descriptor used in the model building for log S is a hybrid optimal descriptor; obtained by combining the two descriptors: (i) molecular graph based descriptor derived from correlation weights of molecular features and (ii) descriptor derived from the simplified molecular input-line entry system (SMILES) code of sweetener molecule. The data set of 315 molecules was divided into four random splits. The four QSAR models which were build for log S using the criterion of IIC were compared with four similar models built "traditional protocol" described elsewhere. The comparison revealed that the models built using IIc were better with statistical performance.

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.

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).

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.