Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability.

PURPOSE: In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure-activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments. METHODS: The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits. RESULTS: A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed. CONCLUSION: The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.

Development of Novel Therapeutics for Schizophrenia Treatment Based on a Selective Positive Allosteric Modulation of α1-Containing GABAARs-In Silico Approach.

For the development of atypical antipsychotics, the selective positive allosteric modulation of the ionotropic GABAA receptor (GABAAR) has emerged as a promising approach. In the presented research, two unrelated methods were used for the development of QSAR models for selective positive allosteric modulation of 1-containing GABAARs with derivatives of imidazo [1,2-a]-pyridine. The development of conformation-independent QSAR models, based on descriptors derived from local molecular graph invariants and SMILES notation, was achieved with the Monte Carlo optimization method. From the vast pool of 0D, 1D, and 2D molecule descriptors, the GA-MLR method developed additional QSAR models. Various statistical methods were utilised for the determination of the developed models' robustness, predictability, and overall quality, and according to the obtained results, all QSAR models are considered good. The molecular fragments that have a positive or negative impact on the studied activity were obtained from the studied molecules' SMILES notations, and according to the obtained results, nine novel compounds were designed. The binding affinities to GABAAR of designed compounds were assessed with the application of molecular docking studies and the obtained results showed a high correlation with results obtained from QSAR modeling. To assess all designed molecules' "drug-likeness", their physicochemical descriptors were computed and utilised for the prediction of medicinal chemistry friendliness, pharmacokinetic properties, ADME parameters, and druglike nature.

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition.

The COVID-19 pandemic emerged in 2019, bringing with it the need for greater stores of effective antiviral drugs. This paper deals with the conformation-independent, QSAR model, developed by employing the Monte Carlo optimization method, as well as molecular graphs and the SMILES notation-based descriptors for the purpose of modeling the SARS-CoV-3CLpro enzyme inhibition. The main purpose was developing a reproducible model involving easy interpretation, utilized for a quick prediction of the inhibitory activity of SAR-CoV-3CLpro. The following statistical parameters were present in the best-developed QSAR model: (training set) R 2 = 0.9314, Q 2 = 0.9271; (test set) R 2 = 0.9243, Q 2 = 0.8986. Molecular fragments, defined as SMILES notation descriptors, that have a positive and negative impact on 3CLpro inhibition were identified on the basis of the results obtained for structural indicators, and were applied to the computer-aided design of five new compounds with (4-methoxyphenyl)[2-(methylsulfanyl)-6,7-dihydro-1H-[1,4]dioxino[2,3-f]benzimidazol-1-yl]methanone as a template molecule. Molecular docking studies were used to examine the potential inhibition effect of designed molecules on SARS-CoV-3CLpro enzyme inhibition and obtained results have high correlation with the QSAR modeling results. In addition, the interactions between the designed molecules and amino acids from the 3CLpro active site were determined, and the energies they yield were calculated. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02170-8.

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.

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.

SMILES-based QSAR models for the calcium channel-antagonistic effect of 1,4-dihydropyridines.

The activity of 72 1,4-dihydropyridines as calcium channel antagonists was examined. The simplified molecular input-line entry system (SMILES) was used as representation of the molecular structure of the calcium channel antagonists. Quantitative structure-activity relationships (QSARs) were developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the training and test sets. Using the Monte Carlo method, the 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 obtained results reveal good predictive potential of the applied approach: The correlation coefficients (r(2) ) for the test sets of the four random splits are 0.9571, 0.9644, 0.9836, and 0.9444.

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.

In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7.

The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development.

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA.

The inhibition of GABAA can be used in general anesthesia. Although, barbiturates and thiobarbiturates are used in anesthesia, the mechanism of their action hasn't been established. QSAR modeling is a wieldy used technique in these cases and this study presents the QSAR modeling for a group of barbiturates and thiobarbiturates with determined anesthetic activity. Developed QSAR models were based on conformation independent and 2D descriptors as well as field contribution. As descriptors used for developing conformation independent QSAR models, (SMILES) notation and local invariants of the molecular graph were used. Monte Carlo optimization method was applied for building QSAR models for two defined activities. Methodology for developing QSAR models capable of dealing with the small dataset that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions was used. Two-dimensional descriptors with definite physicochemical meaning were used and modeling was done with the application of both partial least squares and multiple linear regression models with three latent variables related to simple and interpretable 2D descriptors. Different statistical methods, including novel method - the index of ideality of correlation, were used to test the quality of the developed models, especially robustness and predictability and all obtained results were good. In this study, obtained results indicate that there is a very good correlation between all developed models. Molecular fragments that account for the increase/decrease of a studied activity were defined and further used for the computer-aided design of new compounds as potential anesthetics.

Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition.

Carbonic anhydrase is a metalloprotein, an enzyme with strong inhibition in antibacterial treatment. This study presents QSAR modeling for a series of 41 chemical compounds, 40 sulfonamides and one sulfamate, including 13 clinically tested drugs as carbonic anhydrase inhibitors based on the Monte Carlo optimization with molecular descriptors based on the SMILES notation and local invariants of the molecular graph, and field 3D based methods. Conformation independent QSAR models were developed for three random splits and a 3D QSAR model for one random split into the training and test sets. The statistical quality of the developed models, including robustness and predictability, was tested using various statistical approaches and the results that were obtained were very good. An excellent correlation between the results from the conformation independent and the 3D QSAR model was obtained. A novel statistical metric known as the index of ideality of correlation was used for the final assessment of the model, and the obtained results were good. Molecular fragments responsible for the increases and decreases of a studied activity were defined and further used for the computer-aided design of new compounds as potential carbonic anhydrase inhibitors. Molecular docking was applied for the final assessment of the developed QSAR model and designed inhibitors, and an excellent correlation between the results from QSAR modeling and molecular docking studies was obtained.Communicated by Ramaswamy H. Sarma.

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.

Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition-In silico approach.

Rho kinases, one of the best-known members of the serine/threonine (Ser/Thr) protein kinase family, can be used as target enzymes for the treatment of many diseases such as cancer or multiple sclerosis, and especially for the treatment of cardiovascular diseases. This study presents QSAR modeling for a series of 41 chemical compounds as Rho kinase inhibitors based on the Monte Carlo method. QSAR models were developed for three random splits into the training and test set. Molecular descriptors used for QSAR modeling were based on the SMILES notation and local invariants of the molecular graph. The statistical quality of the developed model, including robustness and predictability, was tested with different statistical approaches and satisfying results were obtained. The best calculated QSAR model had the following statistical parameters: r2 = 0.8825 and q2 = 0.8626 for the training set and r2 = 0.9377 and q2 = 0.9124 for the test set. Novel statistical metric entitled as the index of ideality of correlation was used for the final model assessment, and the obtained results were 0.6631 for the training and 0.9683 for the test set. Molecular fragments responsible for the increases and decreases of the studied activity were defined and they were further used for the computer-aided design of new compounds as potential Rho kinase inhibitors. The final assessment of the developed QSAR model and designed inhibitors was achieved with the application of molecular docking. An excellent correlation between the results from QSAR and molecular docking studies was obtained.

In silico studies and the design of novel agents for the treatment of systemic tuberculosis.

Tuberculosis (TB) is an ancient infectious disease, which re-emerged with the appearance of multidrug-resistant strains and acquired immune deficiency syndrome. Enoyl-acyl-carrier protein reductase (InhA) has emerged as a promising target for the development of anti-tuberculosis therapeutics. This study aims to develop quantitative structure-activity relationship (QSAR) models for a series of arylcarboxamides as InhA inhibitors. The QSAR models were calculated on the basis of optimal molecular descriptors based on the simplified molecular-input line-entry system (SMILES) notation with the Monte Carlo method as a model developer. The molecular docking study was used for the final assessment of the developed QSAR model and designed novel inhibitors. Methods used for the validation indicated that the predictability of the developed model was good. Structural indicators defined as molecular fragments responsible for increases and decreases of the studied activity were defined. The computer-aided design of new compounds as potential InhA inhibitors was presented. The Monte Carlo optimization was capable of being an efficient in silico tool for developing a model of good statistical quality. The predictive potential of the applied approach was tested and the robustness of the model was proven using different methods. The results obtained from molecular docking studies were in excellent correlation with the results from QSAR studies. This study can be useful in the search for novel anti-tuberculosis therapeutics based on InhA inhibition. Communicated by Ramaswamy H. Sarma.

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 anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study.

Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics.

Prediction of gas chromatographic retention indices based on Monte Carlo method.

A new method for the prediction of retention indices using Monte Carlo method and based on local graph invariants and SMILES notation of studied compounds has been presented. Very satisfactory results were obtained with the proposed method, since 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=0.8097 for the training set and r2=0.9372 for the test set. Structural indicators defined responsible for the increases and decreases of gas chromatographic retention indices activity have been 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.