Water–Solvent Partition Coefficients and Δ Log P Values as Predictors for Blood–Brain Distribution; Application of the Akaike Information Criterion

2010 ◽  
Vol 99 (5) ◽  
pp. 2492-2501 ◽  
Author(s):  
Michael H. Abraham ◽  
William E. Acree ◽  
Albert J. Leo ◽  
David Hoekman ◽  
Joseph E. Cavanaugh
Keyword(s):  
Akaike Information Criterion ◽  
Partition Coefficients ◽  
Information Criterion ◽  
Log P ◽  
Brain Distribution ◽  
P Values ◽  
Water Solvent ◽  
Blood Brain

scholarly journals A comparative study concerning chromatographic retention and computed partition coefficients of some precursors of peraza crown ethers

2010 ◽  
Vol 8 (6) ◽  
pp. 1203-1209 ◽  
Author(s):  
Cristina Onişor ◽  
Gabriela Blăniţă ◽  
Maria Coroş ◽  
Monica Bucşa ◽  
Mircea Vlassa ◽  
...  
Keyword(s):  
Comparative Study ◽  
Crown Ethers ◽  
Partition Coefficients ◽  
High Performance ◽  
Correlation Coefficients ◽  
Reversed Phase ◽  
Neural Network Modeling ◽  
Log P ◽  
Chromatographic Retention ◽  
P Values

AbstractRetention indices for some precursors of peraza crown ethers were determined by reversed phase high-performance thin layer chromatography on RP-18 plates with methanol-water in different volume proportions as mobile phase. The Log P values for the same compounds were calculated using different computer programs: SciQSAR, SciLogP, Chem3D Ultra 8.0, XLOGP (based on atom contributions), Chemaxon and KOWWIN (based on atom/fragment contributions), cLogP (based on fragmental contributions), ALOGPS and IAlogP (based on atom-type electrotopological-state indices and neural network modeling). A comparative study concerning lipophilic parameters (RM0, b and ϕ0) and computed partition coefficients has been developed. Taking into account the correlation coefficients between determined and calculated Log P values, it seems that RM0 and b are less suitable than ϕ0 for estimating lipophilicity of the compounds investigated, and cLogP and ALOGPS provide the best correlations with experimental values.

scholarly journals Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

2021 ◽  
Author(s):  
Teresa Danielle Bergazin ◽  
Nicolas Tielker ◽  
Yingying Zhang ◽  
Junjun Mao ◽  
M. R. Gunner ◽  
...  
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Physical Property ◽  
Rational Drug Design ◽  
Log P ◽  
Prediction Methods ◽  
Research Groups ◽  
Reasonable Accuracy ◽  
Statistical Assessment ◽  
Rational Drug

AbstractThe Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Complexity versus simplicity: an example of groundwater model ranking with the Akaike Information Criterion

2012 ◽  
Vol 9 (8) ◽  
pp. 9687-9714 ◽  
Author(s):  
I. Engelhardt ◽  
J. G. De Aguinaga ◽  
H. Mikat ◽  
C. Schüth ◽  
O. Lenz ◽  
...  
Keyword(s):  
Akaike Information Criterion ◽  
Conceptual Models ◽  
Information Criterion ◽  
Model Parameters ◽  
Groundwater Model ◽  
Piezometric Head ◽  
Simulated Groundwater ◽  
Pumping Rates ◽  
Uncalibrated Model ◽  
Piezometric Heads

Abstract. A groundwater model characterized by a lack of field data to estimate hydraulic model parameters and boundary conditions combined with many piezometric head observations was investigated concerning model uncertainty. Different conceptual models with a stepwise increase from 0 to 30 adjustable parameters were calibrated using PEST. Residuals, sensitivities, the Akaike Information Criterion (AIC), and the likelihood of each model were computed. As expected, residuals and standard errors decreased with an increasing amount of adjustable model parameters. However, the model with only 15 adjusted parameters was evaluated by AIC as the best option with a likelihood of 98%, while the uncalibrated model obtained the worst AIC value. Computing of the AIC yielded the most important information to assess the model likelihood. Comparing only residuals of different conceptual models was less valuable and would result in an overparameterization of the conceptual model approach. Sensitivities of piezometric heads were highest for the model with five adjustable parameters reflecting also changes of extracted groundwater volumes. With increasing amount of adjustable parameters piezometric heads became less sensitive for the model calibration and changes of pumping rates were no longer displayed by the sensitivity coefficients. Therefore, when too many model parameters were adjusted, these parameters lost their impact on the model results. Additionally, using only sedimentological data to derive hydraulic parameters resulted in a large bias between measured and simulated groundwater level.

scholarly journals Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

2018 ◽  
Vol 10 (1) ◽  
pp. 80-87
Author(s):  
Surobhi Deka
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Akaike Information Criterion ◽  
Markov Chain Model ◽  
Information Criterion ◽  
Chain Model ◽  
Adequate Model ◽  
First Order ◽  
North East India ◽  
North East

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

scholarly journals Autoregressive Modeling and Forecasts of Degema Monthly Allocation: Buy’s-Ballot and Bartlett’s Transformation

2021 ◽  
pp. 61-75
Author(s):  
Herbert, AfeyaIbibo ◽  
Biu, Oyinebifun Emmanuel ◽  
Enegesele, Dennis ◽  
Wokoma, Dagogo Samuel Allen
Keyword(s):  
Autocorrelation Function ◽  
Akaike Information Criterion ◽  
Selection Criteria ◽  
Linear Trend ◽  
Information Criterion ◽  
Transformation Method ◽  
Autoregressive Modeling ◽  
Data Set ◽  
Trend Pattern ◽  
Partial Autocorrelation Function

The paper focused on Autoregressive modeling and forecasts of Degema Local Government Council Monthly Allocation (DLGCMA) in River State, Nigeria. The Buys-Ballot table and Bartlett’s Transformation method were adopted to identify the trend pattern and to determine the best transformation for the series. The logarithmic transformation was adjudged to be the best and was applied to stabilize the variance. Identification of the trend and stationary for the data set was done and the DLGCMA series showed a linear trend that was non-stationary. The stationarity of the DLGCMA series was obtained after the first difference. The ARIMA models were fitted to the series base on the behaviour of autocorrelation function (ACF) and partial autocorrelation function (PACF). Finally, the model selection criteria called Akaike information criterion was used to determine the best model among the predicted models. The AR(3,1,0) model ( Xt = 0.56Xt-1 + 0.17Xt-2 + 0.64Xt-3 - 0.37Xt-4 + et) was considered to be the best model because it has the least value of the Akaike information criterion (AIC). Hence, the forecasts for the next allocation of twenty-four (24) months ahead were determined.

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