Monte Carlo Optimization Method Research Articles

The heterogeneous effects of transportation infrastructure on trade-embodied CO2 transfer: A multi-scale perspective

This paper investigates the heterogeneous impact of transportation infrastructure on trade-embodied CO2 emission (TEC) transfer. First, a multi-scale input-output (MSIO) model is developed to analyze the TEC from 31 Chinese provinces to 64 Belt and Road Initiative (BRI) countries. Then, the panel quantile regression and Markov Chain Monte Carlo optimization methods are applied to estimate the impact of transportation infrastructure on TEC, taking into account the provincial heterogeneity. The main results are as follows: (1) The TEC of China's exports to BRI countries amounted to 311.01 Mt CO2 annually from 2012 to 2016, with significant variations in TEC characteristics among different Chinese provinces. (2) China's transportation infrastructure network density (TID) predominantly exhibited a negative impact on exported TEC across various quantiles. Specifically, a 1% increase in TID was associated with a decrease in TEC ranging from approximately 0.25% to 3.99%. (3) As quantiles increased, the impact of transportation-converted turnover in China on TEC also rose, with a range of 0.66% to 0.94%. This study identifies the crucial regions and sectors of TEC from a ‘provincial-national-global’ multi-scale perspective, and implies that strengthening the construction of transportation infrastructure may improve the marginal effect of industrial structure transformation and environmental improvement brought about by trade activity.

In silico insights into design of novel VEGFR-2 inhibitors: SMILES-based QSAR modelling, and docking studies on substituted benzo-fused heteronuclear derivatives

ABSTRACT Eight QSAR models (M1–M8) were developed from a dataset of 118 benzo-fused heteronuclear derivatives targeting VEGFR-2 by Monte Carlo optimization method of CORALSEA 2023 software. Models were generated with hybrid optimal descriptors using both SMILES and Graphs with zero- and first-order Morgan extended connectivity index from a training set of 103 derivatives. All statistical parameters for model validation were within the prescribed limits, establishing the models to be robust and of excellent quality. Among all models, split-2 of M5 was the best-fit as reflected by r v a lidation 2 , Q v a lidation 2 and MAE . Mechanistic interpretation of this model assisted the identification of structural descriptors as promoters and hinderers for VEGFR-2 inhibition. These descriptors were utilized to design novel VEGFR-2 inhibitors (YS01-YS07) by bringing modifications in compound MS90 in the dataset. Docking of all designed compounds, MS90 and sorafenib with VEGFR-2 binding site revealed favourable binding interactions. Docking score of YS07 was higher than that of MS90 and sorafenib. Molecular dynamics simulation study revealed sustained interactions of YS07 with key amino acids of VEGFR-2 at a run time of 100 ns. This study concludes the development of a best fit QSAR model which can assist the design of new anticancer agents targeting VEGFR-2.

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

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. 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. 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. The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.

Applying multivariate inverse heat conduction problem to determine thermal contact resistance in aircraft embedded systems

In the field of heat transfer, inverse problems deal with the estimation of parameters that are difficult to measure directly. The usefulness of inverse techniques is such that, due to severe conditions, direct measurement of a certain variable becomes inaccessible. This works aims to perform inverse estimation in two problems. The first case is related to the estimation of the heat flux boundary condition and thermal contact resistance between two SAE 1020 steel plates. The first case is used as validation for the second case and is solved using the finite volume method for the discretization of the diffusion equation and Successive Over Relaxation (SOR) for solving the system of linear eqs. A set of seven one-dimensional experiments were performed varying the roughness and contact pressure at the interface of the samples and, as expected, it was found that the thermal conductance is a function of these parameters. The second case consists in the estimation of three thermal resistances in an aircraft embedded system consisting of four components and ambient air. In this case, the direct problem is solved using fourth-order Runge-Kutta to solve the system of ODEs. In both cases a future times regularization technique approach combined with Markov Chain Monte Carlo (MCMC) optimization method is used to solve the inverse problem. The embedded system inverse problem is also solved using a new and simple approach based on the Quadrilateral Optimization Method (QOM) with future time steps regularization and the result is compared with the MCMC method. The results of this work consolidate a low-cost inverse estimation setup and attest to the capacity of multivariate estimation in inverse heat transfer problems.

Correlation intensity index-index of ideality of correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment

Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer’s and Parkinson's diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (−11.5 kcal mol−1) towards monoamine oxidase-B.

The GMRT archive atomic gas survey – II. Mass modelling and dark matter halo properties across late-type spirals

ABSTRACT Studying the kinematics and mass modelling of galaxies from H i 21 cm data provides valuable insights into the properties of both the baryonic components and the dark matter halo in nearby galaxies. Despite many observational studies, mass modelling of galaxies remains challenging due to different limitations. For example, most of the previous studies involving mass modelling are based on rotation curves derived from 2D velocity fields from H i or H α spectroscopic observation which are often affected by beam smearing and projection effect. However, kinematic modelling done by fitting the ‘Tilted ring model’ to 3D data cube is not affected by these issues. In this study, we present and compare 3D kinematic modelling of a pilot sample of 11 galaxies from the GMRT archive atomic gas survey (GARCIA) using two different publicly available pipelines. We model the observed H i rotation curve using 3.6-μm infrared data and SDSS r-band data for stellar contribution, H i surface density profile for gas, and Navarro–Frenk–White profile for dark matter halo; and employ the Markov chain Monte Carlo optimization method for parameter estimation. Further, to validate our analysis, we revisit important scaling relations, e.g. the Mgas–Mstar relation, Mstar–Mhalo relation, Mgas–Mhalo relation and Baryonic Tully–Fisher relation. The scaling relations from our analysis are broadly consistent with that reported in the literature. A larger sample of galaxies from GARCIA in the near future will allow studying these scaling relations in greater details.

Modeling polyhydroxyalkanoates production from sugarcane vinasse by mixed microbial cultures

Sugarcane vinasse is wastewater suitable for polyhydroxyalkanoates (PHA) production by mixed cultures in a three-stage process. In this work, models representing the selection of PHA-producing microorganisms in a sequencing batch reactor (SBR) submitted to a feast-famine process and uncoupled carbon/nitrogen feeding, as well as an accumulation of PHA in a fed-batch reactor from sugarcane vinasse were developed. Sensitivity analysis of the models allowed to rank the influence of parameters and calibration was carried out applying the Box-Draper method in conjunction with the Markov Chain Monte Carlo optimization method. The optimized selection model was able to describe the SBR under different operational conditions. Optimization for each condition of the accumulation showed that higher CODt/Nitrogen ratios in the SBR induced the selection of a culture with a growth response rather than storage, contributing to elucidate the impact that the CODt/Nitrogen ratio in the SBR has on PHA accumulation.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment

We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two artificial intelligence (AI) virtual agents driven by behavior trees played each game level chunk. We recorded the degree of collaboration required to accomplish each game level chunk by the AI virtual agents and used it to characterize each game level chunk. To synthesize a game level, we assigned to the total cost function cost terms that encode both the degree of collaboration and game level design decisions. Then, we used a Markov-chain Monte Carlo optimization method, called simulated annealing, to solve the total cost function and proposed a design for a game level. We synthesized three game levels (low, medium, and high degrees of collaboration game levels) to evaluate our implementation. We then recruited groups of participants to play the game levels to explore whether they would experience a certain degree of collaboration and validate whether the AI virtual agents provided sufficient data that described the collaborative behavior of players in each game level chunk. By collecting both in-game objective measurements and self-reported subjective ratings, we found that the three game levels indeed impacted the collaboration gameplay behavior of our participants. Moreover, by analyzing our collected data, we found moderate and strong correlations between the participants and the AI virtual agents. These results show that game developers can consider AI virtual agents as an alternative method for evaluating the degree of collaboration required to finish a game level.

A QSAR model for predicting the corneal permeability of drugs – the application of the Monte Carlo optimization method

The Monte Carlo optimization method was employed for the development of a QSAR model for the prediction of corneal 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.

QSAR modelling, molecular docking studies and ADMET predictions of polysubstituted pyridinylimidazoles as dual inhibitors of JNK3 and p38α MAPK

This research paper employs different types of QSAR modeling for the molecules that act as dual inhibitors of JNK3 and p38α MAPK. The SMILES notation and the local molecular graph invariants served as descriptors for QSAR model building, while the Monte Carlo optimization method was assigned the purpose of a model developer. GA-MLR was utilized to obtain a QSAR model from the pool of vast 2D molecule descriptors. To assess the developed models’ quality, robustness, and predictability, several statistical methods were employed, and these yielded favorable results. The molecular fragments which were responsible for the increase/decrease of the examined activity were identified and used for the computer-aided design of new compounds. Molecular docking studies were utilized for the final assessment of the designed inhibitors, highlighting an exceptional correlation with the QSAR modeling results. ADME parameters, pharmacokinetic properties, the drug-like nature, and medicinal chemistry friendliness were predicted by computing physicochemical descriptors to support drug discovery. Based on the obtained results, all the designed molecules possess high drug-likeness.

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) R2 = 0.9314, Q2 = 0.9271; (test set) R2 = 0.9243, Q2 = 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 InformationThe online version contains supplementary material available at 10.1007/s11696-022-02170-8.

Monte Carlo optimization method based QSAR modeling of postmortem redistribution of structurally diverse drugs

The Monte Carlo optimization method was employed for the development of the QSAR model for the prediction for postmortem redistribution of structurally diverse drugs.

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

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.

Quantitative structure toxicity analysis of ionic liquids toward acetylcholinesterase enzyme using novel QSTR models with index of ideality of correlation and correlation contradiction index

Ionic liquids (ILs) have enticed the curiosity of chemists due to their vast applications in academic and industrial research. These have many advantages over other conventional solvents such as broad liquid temperature, negligible vapour pressure, non-volatility, etc. But, from an environmental point of view, these advantages can develop heterogeneous toxic results when released into the environment. It is important to predict the toxicity of ionic liquids. A useful tool for predicting ILs toxicity is the quantitative structure-toxicity relationship (QSTR). The toxicity of ionic liquids is evaluated by predicting the acetylcholinesterase (AChE, EC3.1.1.7) enzyme inhibition. In the present manuscript, an exhaustive QSTR analysis for 229 ionic liquids as an acetylcholinesterase enzyme inhibitor is described using the inbuilt Monte Carlo optimization method of CORAL software. Eleven splits are prepared and from these split, 22 QSTR models are developed using two target functions, i.e. TF1 (without IIC) and TF2 (with IIC). All models developed by TF2 are robust and have better predictability. The model developed for split 1 using TF2 is considered as the best model (RValid2 = 0.7782). In the present work, a novel parameter “Correlation Contradiction Index (CCI)” is studied to recognize its predictability. The docking simulation was also performed to understand the mechanistic interpretation. Further, the mechanistic interpretation of the best QSTR model was in good correlation with the three-dimensional studies of ligand binding. In order to see the true picture of inhibitory potential, ligand transport study of five ILs (IL015, IL040, IL116, IL156 and IL211) was studied in the tunnel leading to the active site of AChE using the services of Caver Web. Result of the transport study showed that these ILs formed a most stable complex in the active site and not in the tunnel and did not obstruct the tunnel for the accessibility of the enzyme active site for the substrate.

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.

Optimization method for calculating the geometrical dimensions of the tooth zone of the switched reluctance motor

Background: Switched reluctance motor (SRM) is a promising type of electromechanical energy converter. It is used in both motor and generator modes. SRM can be used not only in mechanisms with rotational motion, but also as linear motors, including maglev transport systems.
 A lot of publications are devoted to SRM design issues. In development of this direction, further improvement of the SRM design process is proposed using the optimization method of forming the geometrical dimensions of the tooth zone.
 Aim: Reducing of electromagnetic torque ripple SRM by optimizing the geometrical dimensions of the tooth zone.
 Materials and Methods: To simulate magnetic fields, the FEMM 4.2 program was used. The implementation of the optimization algorithm and the processing of the results of field calculations, was carried out in the MATLAB program. The Monte Carlo optimization methods (they rely on random sampling) was taken as the basis for developing the optimization algorithm, and its improvement was achieved using mathematical methods based on probability theory.
 Results: As a result of the optimization calculations, the tooth zone geometry was obtained, which provides a reduction in the pulsation of the SRM electromagnetic torque ripple from 122% to 40%.
 Conclusion: The studies presented in the paper indicate the effectiveness of using the optimization algorithm in designing SRM, as well as the feasibility of removing restrictions on the shape of the tooth SRM and the use of a curved tooth shape, which is determined in the process of solving the optimization problem by a determined criterion.