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.

Synthesis and evaluation of thiophene-based guanylhydrazones (iminoguanidines) efficient against panel of voriconazole-resistant fungal isolates.

A series of new thiophene-based guanylhydrazones (iminoguanidines) were synthesized in high yields using a straightforward two-step procedure. The antifungal activity of compounds was evaluated against a wide range of medicaly important fungal strains including yeasts, molds, and dermatophytes in comparison to clinically used drug voriconazole. Cytotoxic properties of compounds were also determined using human lung fibroblast cell line and hemolysis assay. All guanylhydrazones showed significant activity against broad spectrum of clinically important species of Candida spp., Aspergillus fumigatus, Fusarium oxysporum, Microsporum canis and Trichophyton mentagrophytes, which was in some cases comparable or better than activity of voriconazole. More importantly, compounds 10, 11, 13, 14, 18 and 21 exhibited excellent activity against voriconazole-resistant Candida albicans CA5 with very low minimal inhibitory concentration (MIC) values <2 µg mL(-1). Derivative 14, bearing bromine on the phenyl ring, was the most effective compound with MICs ranging from 0.25 to 6.25 µg mL(-1). However, bis-guanylhydrazone 18 showed better selectivity in terms of therapeutic index values. In vivo embryotoxicity on zebrafish (Danio rerio) showed improved toxicity profile of 11, 14 and 18 in comparison to that of voriconazole. Most guanylhydrazones also inhibited C. albicans yeast to hyphal transition, essential for its biofilm formation, while 11 and 18 were able to disperse preformed Candida biofilms. All guanylhydrazones showed the equal potential to interact with genomic DNA of C. albicans in vitro, thus indicating a possible mechanism of their action, as well as possible mechanism of observed cytotoxic effects. Tested compounds did not have significant hemolytic effect and caused low liposome leakage, which excluded the cell membrane as a primary target. On the basis of computational docking experiments using both human and cytochrome P450 from Candida it was concluded that the most active guanylhydrazones had minimal structural prerequisites to interact with the cytochrome P450 14α-demethylase (CYP51). Promising guanylhydrazone derivatives also showed satisfactory pharmacokinetic profile based on molecular calculations.

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.

In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

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.

Development of QSAR model based on Monte Carlo optimization for predicting GABAA receptor binding of newly emerging benzodiazepines.

Benzodiazepines and their derivatives belong to a category of new psychoactive substances that have been introduced into the continually expanding illicit market. However, there is a notable absence of available pharmacological data for these substances. To gain a deeper understanding of their pharmacology, we employed the Monte Carlo optimization conformation-independent method as a tool for developing QSAR models. These models were built using optimal molecular descriptors derived from both SMILES notation and molecular graph representations. The resulting QSAR model demonstrated robustness and a high degree of predictability, proving to be very reliable. Moreover, we were able to identify specific molecular fragments that exerted both positive and negative effects on binding activity. This discovery paves the way for the swift prediction of binding activity for emerging benzodiazepines, offering a faster and more cost-effective alternative to traditional in vitro/in vivo analyses.

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.

Clinically used antifungal azoles as ligands for gold(III) complexes: the influence of the Au(III) ion on the antimicrobial activity of the complex.

In a search for novel antimicrobial metal-based therapeutic agents, mononuclear gold(III) complexes 1-7 of the general formula [AuCl3(azole)], where azole stands for imidazole (im, 1), 1-isopropylimidazole (ipim, 2), 1-phenylimidazole (phim, 3), clotrimazole (ctz, 4), econazole (ecz, 5), tioconazole (tcz, 6) and voriconazole (vcz, 7) were synthesized, characterized and biologically evaluated. In all complexes, the corresponding azole ligand is monodentately coordinated to the Au(III) via the imidazole or triazole nitrogen atom, while the remaining coordination sites are occupied by chloride anions leading to the square-planar arrangement. In vitro antimicrobial assays showed that the complexation of inactive azoles, imidazole, 1-isopropylimidazole and 1-phenylimidazole, to the Au(III) ion led to complexes 1-3, respectively, with moderate activity against the investigated strains and low cytotoxicity on the human normal lung fibroblast cell line (MRC-5). Moreover, gold(III) complexes 4-7 with clinically used antifungal agents clotrimazole, econazole, tioconazole and voriconazole, respectively, have, in most cases, enhanced antimicrobial effectiveness relative to the corresponding azoles, with the best improvement achieved after complexation of tioconazole (6) and voriconazole (7). The complexes 4-7 and the corresponding antifungal azoles inhibited the growth of dermatophyte Microsporum canis at 50 and 25 µg mL-1. Gold(III) complexes 1-3 significantly reduced the amount of ergosterol in the cell membrane of Candida albicans at the subinhibitory concentration of 0.5 × MIC (minimal inhibitory concentration), while the corresponding imidazole ligands did not significantly affect the ergosterol content, indicating that the mechanism of action of the gold(III)-azole complexes is associated with inhibition of ergosterol biosynthesis. Finally, complexes 5 and 6 significantly reduced the production of pyocyanin, a virulence factor in Pseudomonas aeruginosa controlled by quorum sensing, and increased cell survival after exposure to this bacterium. These findings could be of importance for the development of novel gold(III)-based antivirulence therapeutic agents that attenuate virulence without pronounced effect on the growth of the pathogens, offering a lower risk for resistance development.

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.

Copper(II) and Zinc(II) Complexes with the Clinically Used Fluconazole: Comparison of Antifungal Activity and Therapeutic Potential.

Copper(II) and zinc(II) complexes with clinically used antifungal drug fluconazole (fcz), {[CuCl2(fcz)2].5H2O}n, 1, and {[ZnCl2(fcz)2]·2C2H5OH}n, 2, were prepared and characterized by spectroscopic and crystallographic methods. The polymeric structure of the complexes comprises four fluconazole molecules monodentately coordinated via the triazole nitrogen and two chlorido ligands. With respect to fluconazole, complex 2 showed significantly higher antifungal activity against Candida krusei and Candida parapsilosis. All tested compounds reduced the total amount of ergosterol at subinhibitory concentrations, indicating that the mode of activity of fluconazole was retained within the complexes, which was corroborated via molecular docking with cytochrome P450 sterol 14α-demethylase (CYP51) as a target. Electrostatic, steric and internal energy interactions between the complexes and enzyme showed that 2 has higher binding potency to this target. Both complexes showed strong inhibition of C. albicans filamentation and biofilm formation at subinhibitory concentrations, with 2 being able to reduce the adherence of C. albicans to A549 cells in vitro. Complex 2 was able to reduce pyocyanin production in Pseudomonas aeruginosa between 10% and 25% and to inhibit its biofilm formation by 20% in comparison to the untreated control. These results suggest that complex 2 may be further examined in the mixed Candida-P. aeruginosa infections.

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.

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach.

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.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.