The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

2020 ◽  
Vol 16 (3) ◽  
pp. 197-206 ◽  
Author(s):  
Andrey A. Toropov ◽  
Alla P. Toropova
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Target Function ◽  
Statistical Characteristics ◽  
Mathematical Function ◽  
Coral Software ◽  
Structure Property ◽  
Molecular Features ◽  
The Monte Carlo Method ◽  
Correlation Weights

Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Application of the Monte Carlo Method for the Prediction of Behavior of Peptides

2019 ◽  
Vol 20 (12) ◽  
pp. 1151-1157 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Natural Sciences ◽  
Monte Carlo Technique ◽  
Quantitative Structure ◽  
Structure Property ◽  
The Monte Carlo Method ◽  
Modern Natural

Prediction of physicochemical and biochemical behavior of peptides is an important and attractive task of the modern natural sciences, since these substances have a key role in life processes. The Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers); (ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii) development of databases on the biopolymers. Current ideas related to application of the Monte Carlo technique for studying peptides and biopolymers have been discussed in this review.

QSPR/QSAR Analyses by Means of the CORAL Software

2015 ◽  
pp. 560-585 ◽  
Author(s):  
Andrey A. Toropov ◽  
Alla P. Toropova ◽  
Emilio Benfenati ◽  
Orazio Nicolotti ◽  
Angelo Carotti ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Molecular Graph ◽  
Qsar Model ◽  
Mathematical Function ◽  
Coral Software ◽  
Structure Property ◽  
Qsar Analysis ◽  
The Monte Carlo Method ◽  
Optimal Descriptor ◽  
New Ideas

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Paper 35: Some Aspects of Measurement of Roundness

1967 ◽  
Vol 182 (11) ◽  
pp. 425-429 ◽  
Author(s):  
A. N. Avdulov ◽  
Y. L. Polunov ◽  
A. N. Tabenkin ◽  
I. A. Faradjev ◽  
V. G. Shuster
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Digital Computer ◽  
Statistical Characteristics ◽  
The Monte Carlo Method

This paper deals with the analysis of the extent to which the results obtained from the measurements of the same graph may vary while using different reference circles. The statistical characteristics of the graph were obtained experimentally, and the algorithms for circles were worked out. The analysis was carried out by the Monte-Carlo method on a digital computer.

scholarly journals QSPR/QSAR Analyses by Means of the CORAL Software

2017 ◽  
pp. 929-955
Author(s):  
Andrey A. Toropov ◽  
Alla P. Toropova ◽  
Emilio Benfenati ◽  
Orazio Nicolotti ◽  
Angelo Carotti ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Molecular Graph ◽  
Qsar Model ◽  
Mathematical Function ◽  
Coral Software ◽  
Structure Property ◽  
Qsar Analysis ◽  
The Monte Carlo Method ◽  
Optimal Descriptor ◽  
New Ideas

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Study on Critical Corrosive Ratio of Reinforced Concrete Based on Monte-Carlo Method

2011 ◽  
Vol 105-107 ◽  
pp. 1060-1064
Author(s):  
Yue Shun Chen ◽  
Li Liu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Model Simulation ◽  
Protective Layer ◽  
Statistical Characteristics ◽  
Constant Current ◽  
Accelerated Corrosion ◽  
The Monte Carlo Method ◽  
Good Agreement

Based on the relationship between the displacement and the corresponding strain in protective layer concrete during their corrosive expanding, the strain in protective layer arrives the critical strain have been think as the cracking condition, then a certainty critical corrosive ratio model has been constructed in this paper. Considering the probability distribution and the statistical characteristics of concrete protective layer thickness c, reinforced bar diameter dst and the effectively volume expansion coefficient neff, the Monte-Carlo method have been used to simulate the critical corrosive ratio ρcr, its probability distribution has been shown in good agreement with Weibull distribution and lognormal distribution in results. Through the constant current polarization accelerated corrosion test, the critical corrosive model has been validated. With comparison of critical corrosive ratio which be obtained with the test and the model simulation results respectively, where a good agreement have been shown.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

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