scholarly journals QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CLpro Inhibitors against MERS-CoV

Chemistry ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 391-401
Author(s):  
Imad Hammoudan ◽  
Soumaya Matchi ◽  
Mohamed Bakhouch ◽  
Salah Belaidi ◽  
Samir Chtita
Keyword(s):  
External Validation ◽  
Predictive Ability ◽  
Original Data ◽  
Predictive Capacity ◽  
Data Set ◽  
Qsar Modelling ◽  
Test Sets ◽  
Components Analysis ◽  
The Relationship ◽  
Mlr Model

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R2 = 0.691, cross-validation parameter Q2cv = 0.528 and the external validation parameter R2test = 0.794.

scholarly journals Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity

2021 ◽  
Vol 22 (15) ◽  
pp. 8352
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Manoj K. Sabnani ◽  
Abdul Samad
Keyword(s):  
External Validation ◽  
Factor Xa ◽  
Predictive Ability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Data Set ◽  
Qsar Analysis ◽  
Lead Compounds ◽  
Charged Ring ◽  
Life Threatening

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

scholarly journals The Genealogy of Law

2012 ◽  
Vol 20 (3) ◽  
pp. 329-350 ◽  
Author(s):  
Tom S. Clark ◽  
Benjamin E. Lauderdale
Keyword(s):  
Judicial Politics ◽  
Original Data ◽  
Court Decisions ◽  
Family Tree ◽  
Data Set ◽  
Doctrinal Development ◽  
Statistical Assumptions ◽  
The Relationship ◽  
Development And Evolution ◽  
Theoretical Conceptualization

Many theories of judicial politics have at their core the concepts of legal significance, doctrinal development and evolution, and the dynamics of precedent. Despite rigorous theoretical conceptualization, these concepts remain empirically elusive. We propose the use of a genealogical model (or “family tree”) to describe the Court's construction of precedent over time. We describe statistical assumptions that allow us to estimate this kind of structure using an original data set of citation counts between Supreme Court majority opinions. The genealogical model of doctrinal development provides a parsimonious description of the dependencies between opinions, while generating measures of legal significance and other related quantities. We employ these measures to evaluate the robustness of a recent finding concerning the relationship between ideological homogeneity within majority coalitions and the legal impact of Court decisions.

GENERATING BALANCED LEARNING AND TEST SETS FOR FUNCTION APPROXIMATION PROBLEMS

2011 ◽  
Vol 21 (03) ◽  
pp. 247-263 ◽  
Author(s):  
J. P. FLORIDO ◽  
H. POMARES ◽  
I. ROJAS
Keyword(s):  
Function Approximation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Original Data ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Out Of Sample ◽  
Test Sets ◽  
Approximation Problems

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

Local Improvement Districts as Political Participation Signals

2020 ◽  
pp. 147892992096578
Author(s):  
Dan Ziebarth
Keyword(s):  
New York ◽  
Political Participation ◽  
Voter Turnout ◽  
Socioeconomic Development ◽  
Local Politics ◽  
Original Data ◽  
Data Set ◽  
Local Improvement ◽  
State And Local ◽  
The Relationship

A significant amount of literature has inspected the relationship between public–private partnerships and state and local government. This literature has focused primarily on how these agreements shape financing, economic development, and public policy measures. There is little research, however, on how improvement districts may affect political participation. There are many reasons to believe that these districts may raise levels of political participation, as they deeply affect state and local politics and shape the socioeconomic development of local communities. This article fills this gap in the literature by exploring the relationship between the establishment of local improvement districts and voter participation rates. An original data set is constructed from 18 state assembly districts and 22 local improvement districts in New York City across nine elections between 2002 and 2018, resulting in 198 unique observations across time. Empirical results reflect how the development of improvement districts can serve as signals for rising political participation in surrounding areas, marked by increasing rates of voter turnout across midterm and presidential-year election cycles. These findings are compelling, providing insight into how local organizations designed and sustained through issue ownership and community collaboration have the ability to raise political participation through electoral activity.

Financial Needs for a Competitive Business Model in the Knowledge Society

2010 ◽  
Vol 1 (4) ◽  
pp. 69-79 ◽  
Author(s):  
David Castillo-Merino ◽  
Dolors Plana-Erta
Keyword(s):  
Business Model ◽  
Financial Constraints ◽  
Knowledge Society ◽  
Financial Structure ◽  
Original Data ◽  
Investment Model ◽  
Data Set ◽  
Innovative Firms ◽  
The Relationship ◽  
Over Time

This paper investigates the constraints for companies to innovate in order to be competitive in the knowledge society. Using a large and original data set of Catalan firms, the authors have conducted a micro econometric analysis following Henry et al.’s (1999) investment model and von Kalckreuth (2004) methodology empirically contrasting the relationship between firms’ investment spread over time and their financial structure. Results show that it exits a positive and significant relationship between firms’ investment shift and financial structure, emerging financial constraints for more innovative firms. Furthermore, these constraints are higher for micro companies and firms within the knowledge-advanced services’ industry. Finally, the authors find that advanced ICT uses by more innovative firms allow them to reduce constraints of access to sources of finance.

Contingent technocracy: bureaucratic independence in developing countries

2016 ◽  
Vol 37 (4) ◽  
pp. 401-429 ◽  
Author(s):  
Manuel P. Teodoro ◽  
M. Anne Pitcher
Keyword(s):  
Developing Countries ◽  
Policy Implementation ◽  
Original Data ◽  
Political Context ◽  
Authoritarian Regimes ◽  
Conventional Wisdom ◽  
The Political ◽  
Data Set ◽  
Established Democracies ◽  
The Relationship

AbstractThis study investigates the effects of formal bureaucratic independence under varying democratic conditions. Conventional accounts predict that greater formal independence of technocratic agencies facilitates policy implementation, but those claims rest on observations of industrialised, high-income countries that are also established democracies. On the basis of research in developing countries, we argue that the effects of agency independence depend on the political context in which the agency operates. Our empirical subjects are privatisation agencies and their efforts to privatise state-owned enterprises in Africa. We predict that greater independence leads to more thorough privatisation under authoritarian regimes, but that the effect of independence declines as a country becomes more democratic. Using an original data set, we examine the relationship between formal agency independence and privatisation in Africa from 1990 to 2007. Our results modify the conventional wisdom on bureaucratic independence and culminate in a more nuanced theory of “contingent technocracy”.

scholarly journals Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling

2021 ◽  
Vol 9 ◽  
Author(s):  
Davood Gheidari ◽  
Morteza Mehrdad ◽  
Mahboubeh Ghahremani
Keyword(s):  
Candida Albicans ◽  
Quantitative Structure Activity Relationship ◽  
Multidrug Resistant ◽  
Qsar Modeling ◽  
Data Set ◽  
Qsar Modelling ◽  
Leave One Out ◽  
Derivatives Of ◽  
Mlr Model

Candida albicans is a pathogenic opportunistic yeast found in the human gut flora. It may also live outside of the human body, causing diseases ranging from minor to deadly. Candida albicans begins as a budding yeast that can become hyphae in response to a variety of environmental or biological triggers. The hyphae form is responsible for the development of multidrug resistant biofilms, despite the fact that both forms have been associated to virulence Here, we have proposed a linear and SPA-linear quantitative structure activity relationship (QSAR) modeling and prediction of Candida albicans inhibitors. A data set that consisted of 60 derivatives of benzoxazoles, benzimidazoles, oxazolo (4, 5-b) pyridines have been used. In this study, that after applying the leverage analysis method to detect outliers’ molecules, the total number of these compounds reached 55. SPA-MLR model shows superiority over the multiple linear regressions (MLR) by accounting 90% of the Q2 of anti-fungus derivatives ‘activity. This paper focuses on investigating the role of SPA-MLR in developing model. The accuracy of SPA-MLR model was illustrated using leave-one-out (LOO). The mean effect of descriptors and sensitivity analysis show that RDF090u is the most important parameter affecting the as behavior of the inhibitors of Candida albicans.

scholarly journals Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256882
Author(s):  
Luciano Antonio de Oliveira ◽  
Carlos Pereira da Silva ◽  
Alessandra Querino da Silva ◽  
Cristian Tiago Erazo Mendes ◽  
Joel Jorge Nuvunga ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Block Design ◽  
Maximum Entropy Principle ◽  
Simulated Data ◽  
Predictive Ability ◽  
Information Criteria ◽  
Interaction Matrix ◽  
Environment Interaction ◽  
Predictive Capacity ◽  
Data Set

The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.

scholarly journals Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

2015 ◽  
Vol 18 (3) ◽  
pp. 515 ◽  
Author(s):  
Zvetanka Dobreva Zhivkova ◽  
Tsvetelina Mandova ◽  
Irini Doytchinova
Keyword(s):  
External Validation ◽  
Model Development ◽  
Predictive Ability ◽  
Structural Features ◽  
Volume Of Distribution ◽  
Pharmacokinetic Parameters ◽  
Basic Drugs ◽  
Quantitative Structure ◽  
Data Set ◽  
Rapid Discrimination

Purpose. The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure – pharmacokinetics relationships (QSPkR) for VDss of basic drugs. Methods: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. Results: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. Conclusions: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

scholarly journals AMOSTRAS VIRTUAIS DE ATRIBUTOS DO SOLO COMO SUBSÍDIO AO PLANEJAMENTO PARA ANÁLISE GEOESTATÍSTICA

2020 ◽  
Vol 35 (3) ◽  
pp. 426-436
Author(s):  
Diego Augusto de campos Moraes ◽  
Anderson Antônio da Conceição Sartori
Keyword(s):  
Cross Validation ◽  
External Validation ◽  
Original Data ◽  
Soil Samples ◽  
Geostatistical Analysis ◽  
Validation Process ◽  
Data Set ◽  
Soil Attributes ◽  
Virtual Samples ◽  
Thiessen Polygons

AMOSTRAS VIRTUAIS DE ATRIBUTOS DO SOLO COMO SUBSÍDIO AO PLANEJAMENTO PARA ANÁLISE GEOESTATÍSTICA   DIEGO AUGUSTO DE CAMPOS MORAES1, ANDERSON ANTÔNIO DA CONCEIÇÃO SARTORI2   1 Professor Doutor, Departamento de Análise e Desenvolvimento de Sistemas, Faculdade Eduvale de Avaré, Av. Prefeito Misael Eufrásio Leal, 347 - Centro, Avaré - SP, 18705-050, [email protected]. 2 Professor Doutor, Grupo de Estudos e Pesquisas Agrárias Georreferenciadas, Faculdade de Ciências Agronômicas de Botucatu – FCA/UNESP, Avenida Universitária, 3780, Altos do Paraíso, Botucatu – SP, 18610-034, [email protected].   RESUMO: O objetivo deste artigo foi propor uma metodologia de amostragem virtual para atributos do solo em área agrícola, a qual pode subsidiar o planejamento para análise geoestatística. Foram selecionadas, aleatoriamente, 23 amostras de solo (profundidades de 0-20 cm e 20-40 cm) do conjunto de dados original, com o objetivo de realizar a validação externa. Foi aplicado o procedimento de polígonos de Thiessen com base nas demais amostras originais do solo (47 amostras) e, em seguida, foram inseridas, aleatoriamente, amostras virtuais (53 amostras). A análise do variograma, validação cruzada, krigagem ordinária e validação externa foram executadas com a finalidade de verificar a robustez da metodologia. A inserção de amostras virtuais mostrou-se promissora, uma vez que o GDE (Grau de Dependência Espacial) e a validação cruzada dos atributos do solo foram aprimorados, situação que não foi observada nos dados originalmente amostrados. A validação externa obteve bons resultados, indicando que a amostragem virtual pode ser utilizada unicamente no planejamento para análise geoestatística.    Palavras-chaves: variograma, validação cruzada, solos.   VIRTUAL SAMPLES OF SOIL ATTRIBUTES AS A SUBSIDY FOR GEOSTATISTICAL ANALYSIS PLANNING   ABSTRACT: The aim of this article was to propose a virtual sampling methodology for soil attributes in an agricultural area, which can support planning for geostatistical analysis. Twenty-three soil samples (depths of 0-20 cm and 20-40 cm) from the original data set were selected randomly, for an external validation process. The Thiessen polygons procedure was applied based on the remaining original soil samples (47 samples), and then, virtual samples (53 samples) were randomly inserted. The analysis of the variogram, cross-validation, ordinary kriging and external validation were performed in order to verify the robustness of the methodology. The insertion of virtual samples was promising, since the GDE (Degree of Spatial Dependence) and the cross-validation of soil attributes were improved, which was not observed in the data originally sampled. The external validation obtained good results, indicating that the virtual sampling can be used only in the planning for geostatistical analysis.   Keywords: variogram, cross-validation, soil.

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