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.

Chemical characterization, in vitro biological activity of essential oils and extracts of three Eryngium L. species and molecular docking of selected major compounds.

Many Eryngium species have been traditionally used as ornamental, edible or medicinal plants. The gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS) analyses have shown that the major compounds in the aerial parts were spathulenol (in E. campestre and E. palmatum oils) and germacrene D (in E. amethystinum oil). The main compounds in the root oil were nonanoic acid, 2,3,4-trimethylbenzaldehyde and octanoic acid for E. campestre, E. amethystinum and E. palmatum, respectively. All the oils expressed the highest potential against Gram-positive bacteria Staphylococcus aureus as well as Gram-negative Klebsiella pneumoniae and Proteus mirabilis. Molecular docking analysis was used for determining a potential antibacterial activity mechanism of compounds present in the essential oils. Molecular docking confirmed that the binding affinity of spathulenol to the active site of tyrosyl-tRNA synthetase was the highest among the tested dominant compounds. Regarding the total phenolic content (determined by the Folin-Ciocalteu assay) and flavonoid content (evaluated using aluminum nitrate nonahydrate), the highest amount was found in the ethyl acetate extract of E. palmatum. The results of DPPH and ABTS assay indicated that the highest antioxidant activity was present in the water extract of E. amethystinum. Extracts of the aerial parts presented as minimum inhibitory concentration (MIC) expressed the activity in the range 0.004-20.00 mg/mL, with the highest activity exhibited by the acetone and ethyl acetate extracts against Proteus mirabilis. The obtained results suggest that Eryngium species may be considered a beneficial native source of the compounds with antioxidant and antimicrobial properties.

Potent anti-melanogenic activity and favorable toxicity profile of selected 4-phenyl hydroxycoumarins in the zebrafish model and the computational molecular modeling studies.

7-Hydroxy-4-phenylcoumarin (7C) and 5,7-dihydroxy-4-phenylcoumarin (5,7C) have been evaluated as potential anti-melanogenic agents in the zebrafish (Danio rerio) model in comparison to commercially utilized depigmenting agents hydroquinone and kojic acid. 7C and 5,7C decreased the body pigmentation at 5 µg/mL, while did not affect the embryos development and survival at doses ≤50 µg/mL and ≤25 µg/mL. Unlike hydroquinone and kojic acid, 4-phenyl hydroxycoumarins were no melanocytotoxic, showed no cardiotoxic side effects, neither caused neutropenia in zebrafish embryos, suggesting these compounds may present novel skin-whitening agents with improved pharmacological properties. Inhibition of tyrosinase was identified as the possible mode of anti-melanogenic action. Molecular docking studies using the homology model of human tyrosinase as well as adenylate cyclase revealed excellent correlation with experimentally obtained results.

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions.

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.

Monte carlo method-based QSAR modeling of penicillins binding to human serum proteins.

The binding of penicillins to human serum proteins was modeled with optimal descriptors based on the Simplified Molecular Input-Line Entry System (SMILES). The concentrations of protein-bound drug for 87 penicillins expressed as percentage of the total plasma concentration were used as experimental data. The Monte Carlo method was used as a computational tool to build up the quantitative structure-activity relationship (QSAR) model for penicillins binding to plasma proteins. One random data split into training, test and validation set was examined. The calculated QSAR model had the following statistical parameters: r(2) = 0.8760, q(2) = 0.8665, s = 8.94 for the training set and r(2) = 0.9812, q(2) = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r(2) = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r(2) = 0.921 and s = 7.18. SMILES-based molecular fragments (structural indicators) responsible for the increase and decrease of penicillins binding to plasma proteins were identified. The possibility of using these results for the computer-aided design of new penicillins with desired binding properties is presented.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models.

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method.

A method for the prediction of retention indices of pesticides using the Monte Carlo method and with optimal molecular descriptors based on local graph invariants and the SMILES notation of studied compounds has been presented. Quite satisfactory results were obtained with the proposed method, since a robust model with good statistical quality was developed. The predictive potential of the applied approach was tested and the robustness of the model was proven with different methods. The best calculated QSPR model had following statistical parameters: r2 = 0.9182 for the training set and r2 = 0.8939 for the test set. Structural indicators defined as molecular fragments responsible for the increases and decreases of gas chromatographic retention indices activity were calculated.

The Monte Carlo technique as a tool to predict LOAEL.

Quantitative structure - activity relationships (QSARs) for the Lowest Observed Adverse Effect Level (LOAEL) for a large set of organic compounds (n = 341) are suggested. The molecular structures of these compounds are represented by Simplified Molecular Input-Line Entry Systems (SMILES). A criteria for the estimation quality of split into the "visible" training set (used for developing a model) and "invisible" external validation set is suggested. The correlation between the above criterion and the predictive potential of developed QSAR model (root-mean-square error for "invisible" validation set) has been detected. One-variable models are built up for several different splits into the "visible" training set and "invisible" validation set. The statistical quality of these models is quite good. Mechanistic interpretation and the domain of applicability for these models are defined according to probabilistic point of view. The methodology for defining applicability domain in QSAR modeling with SMILES notation based optimal descriptors is presented.

Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna.

Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r2 = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.

QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids.

Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure-activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n=7, r(2)=0.8067, s=0.248 (split 1); n=6, r(2)=0.8319, s=0.169 (split 2); and n=6, r(2)=0.6996, s=0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides.

QSAR as a random event: a case of NOAEL.

Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the "visible" training set and "invisible" validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r (2) = 0.718, q (2) = 0.712, s = 0.403, F = 454 (training set); n = 17, r (2) = 0.544, s = 0.367 (calibration set); n = 21, r (2) = 0.61, s = 0.44, r m (2) = 0.61 (validation set); split 2 n = 169, r (2) = 0.711, q (2) = 0.705, s = 0.409, F = 411 (training set); n = 27, r (2) = 0.512, s = 0.461 (calibration set); n = 22, r (2) = 0.669, s = 0.360, r m (2) = 0.63 (validation set); split 3 n = 172, r (2) = 0.679, q (2) = 0.672, s = 0.420, F = 360 (training set); n = 19, r (2) = 0.617, s = 0.582 (calibration set); n = 21, r (2) = 0.627, s = 0.367, r m (2) = 0.54 (validation set). All models are built according to OCED principles.

Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design.

SMILES notation based optimal descriptors as a universal tool for the QSAR analysis with further application in drug discovery and design is presented. The basis of this QSAR modeling is Monte Carlo method which has important advantages over other methods, like the possibility of analysis of a QSAR as a random event, is discussed. The advantages of SMILES notation based optimal descriptors in comparison to commonly used descriptors are defined. The published results of QSAR modeling with SMILES notation based optimal descriptors applied for various pharmacologically important endpoints are listed. The presented QSAR modeling approach obeys OECD principles and has mechanistic interpretation with possibility to identify molecular fragments that contribute in positive and negative way to studied biological activity, what is of big importance in computer aided drug design of new compounds with desired activity.

In silico prediction of the ß-cyclodextrin complexation based on Monte Carlo method.

In this study QSPR models were developed to predict the complexation of structurally diverse compounds with ß-cyclodextrin based on SMILES notation optimal descriptors using Monte Carlo method. The predictive potential of the applied approach was tested with three random splits into the sub-training, calibration, test and validation sets and with different statistical methods. Obtained results demonstrate that Monte Carlo method based modeling is a very promising computational method in the QSPR studies for predicting the complexation of structurally diverse compounds with ß-cyclodextrin. The SMILES attributes (structural features both local and global), defined as molecular fragments, which are promoters of the increase/decrease of molecular binding constants were identified. These structural features were correlated to the complexation process and their identification helped to improve the understanding for the complexation mechanisms of the host molecules.

Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors.

The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r(2)=0.8617 for the training set; r(2)=0.8659, and r(m)(2)=0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r(2)=0.9435, for the training, r(2)=0.9262 and r(m)(2)=0.8199 for the test and r(2)=0.8418, r(av)(m)(2)=0.7469 and ∆r(m)(2)=0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3ß inhibitors has been presented by using defined structural alerts.

Selected 4-phenyl hydroxycoumarins: in vitro cytotoxicity, teratogenic effect on zebrafish (Danio rerio) embryos and molecular docking study.

A study of structure cytotoxic-activity relationship of three hydroxy 4-phenyl-coumarins and basic coumarin molecule against two human cell lines (MRC5 fibroblasts and A375 melanoma cells) is presented. Of all investigated compounds the highest cytotoxic activity in both cell lines was determined for 7,8-dihydroxy-4-phenyl coumarin. SAR studies revealed the influence of phenyl group and hydroxyl group's number and position on cytotoxic activity. In addition, to get an insight about their binding preferences at the active site of the receptor (catalytic subunit of cAMP-dependent protein kinase) molecular docking studies were performed. Docking studies suggest that 4-phenyl hydroxycoumarins are potent cAMP-dependent protein kinase inhibitors, better than their analogs without phenyl group. The teratogenic potential was assessed in zebrafish embryo toxicity test and results showed that 4-phenyl dihydroxycoumarins were more while 7-hydroxy-4-phenyl coumarin was less embryo toxic in comparison to coumarin. In order to examine selected 4-phenyl hydroxycoumarins as a new lead compounds the druglikeness of selected 4-phenyl hydroxycoumarins was estimated by using Lipinski's "rule of five". All selected 4-phenyl hydroxycoumarins proved to have satisfying pharmacokinetic profile.

The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials - two examples of application.

The theoretical predictions of endpoints related to nanomaterials are attractive and more efficient alternatives for their experimental determinations. Such type of calculations for the "usual" substances (i.e. non nanomaterials) can be carried out with molecular graphs. However, in the case of nanomaterials, descriptors traditionally used for the quantitative structure--property/activity relationships (QSPRs/QSARs) do not provide reliable results since the molecular structure of nanomaterials, as a rule, cannot be expressed by the molecular graph. Innovative principles of computational prediction of endpoints related to nanomaterials extracted from available eclectic data (technological attributes, conditions of the synthesis, etc.) are suggested, applied to two different sets of data, and discussed in this work.

QSAR Models for the Reactivation of Sarin Inhibited AChE by Quaternary Pyridinium Oximes Based on Monte Carlo Method.

For three random splits, one-variable models of oximes reactivation of sarin inhibited acetylcholinesterase (logarithm of the AChE reactivation percentage by oximes with concentration of 0.001 M) have been calculated with CORAL software. The total number of considered oximes was 42. Simplified molecular input line entry system (SMILES) and hydrogen-suppressed graph (HSG) are used to represent the molecular structure. Using CORAL software by means of the calculation with Monte Carlo optimization of the so called correlation weights for the molecular fragments, optimal SMILES-based descriptors were defined, which are correlated with an endpoint for the training set. The predictability of these descriptors for an external test are estimated. In this study hybrid representation HSG together with SMILES was used. The "classic" scheme (i.e. split data into the training set and test set) of building up quantitative structure-activity relationships was employed. Computational experiments indicated that this approach can satisfactorily predict the desired endpoint. Best model had following statistical characteristics n=32, r2= 0.6012, s= 0.279, F= 45 for training and n=10, r2= 0.9301, s= 0.076, Rm2=0.9206 for test set.

QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method.

A series of 107 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) with anti-HIV-1 activity as a non-nucleoside reverse transcriptase inhibitor (NNRTI) has been studied. Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships (QSAR) for anti-HIV-1 activity. The QSAR models were calculated with the representation of the molecular structure by simplified molecular input-line entry system and by the molecular graph. Three various splits into training and test set were examined. Statistical quality of all build models is very good. Best calculated model had following statistical parameters: for training set r(2) = 0.8818, q(2) = 0.8774 and r(2) = 0.9360, q(2) = 0.9243 for test set. Structural indicators (alerts) for increase and decrease of the IC50 are defined. Using defined structural alerts computer aided design of new potential anti-HIV-1 HEPT derivates is presented.

Antioxidant properties of selected 4-phenyl hydroxycoumarins: Integrated in vitro and computational studies.

A study on the structure-activity relationship of three hydroxy 4-phenyl coumarins, carried out by employing a series of different chemical cell-free tests is presented. Different assays involving one redox reaction with the oxidant (DPPH, ABTS, FRAP and CUPRAC) were employed. Further, the measurement of inhibition of oxidative degradation, such as lipid peroxidation, was used to define compound antioxidant activity. Our results confirm the good antioxidant activity of the 7,8-dihydroxy-4-phenyl coumarin and moderate antioxidant activity of 5,7-dihydroxy-4-phenyl coumarin. In this work, quantum chemical calculations based on density functional theory have been employed at B3LYP/6-311++G(d,p) level of theory to study the influence of number and position of hydroxyl groups in coumarin molecules on antioxidant activity. Calculated values for HOMO and LUMO energies, energy gap, stabilization energies and spin density distribution confirmed experimental results and were used for SAR definition. For determination of reaction mechanism in gas phase and selected solvents bond dissociation enthalpy, adiabatic ionization potential, proton dissociation enthalpy, proton affinity, electron transfer enthalpy and gas phase acidity have been calculated. Hydrogen Atom Transfer mechanism in vacuum and Single-Electron Transfer followed by the Proton Transfer mechanism in other studied systems are most probable free radical scavenging pathways. On the basis of these findings, these hydroxy 4-phenyl coumarins may be considered as potential therapeutic candidates for pathological conditions characterized by free radical overproduction.

QSAR models for the reactivation of sarin inhibited acetylcholinesterase by quaternary pyridinium oximes based on Monte Carlo method.

Monte Carlo method has been used as a computational tool for building QSAR models for the reactivation of sarin inhibited acetylcholinesterase (AChE) by quaternary pyridinium oximes. Simplified molecular input line entry system (SMILES) together with hydrogen-suppressed graph (HSG) was used to represent molecular structure. Total number of considered oximes was 46 and activity was defined as logarithm of the AChE reactivation percentage by oximes with concentration of 0.001 M. One-variable models have been calculated with CORAL software for one data split into training, calibration and test set. Computational experiments indicated that this approach can satisfactorily predict the desired endpoint. Best QSAR model had the following statistical parameters: for training set r2=0.7096, s=0.177, MAE=0.148; calibration set: r2=0.6759, s=0.330, MAE=0.271 and test set: r2=0.8620, s=0.182, MAE=0.150. Structural indicators (SMILES based molecular fragments) for the increase and the decrease of the stated activity are defined. Using defined structural alerts computer aided design of new oxime derivatives with desired activity is presented.