Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

Classification of Hungarian medieval silver coins using x-ray fluorescent spectroscopy and multivariate data analysis

A set of silver coins from the collection of Déri Museum Debrecen (Hungary) was examined by X-ray fluorescent elemental analysis with the aim to assign the coins to different groups with the best possible precision based on the acquired chemical information and to build models, which arrange the coins according to their historical periods. Results: Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, classification and regression trees and multivariate curve resolution with alternating least squares were applied to reveal dominant pattern in the data and classify the coins into several groups. We also identified those chemical components, which are present in small percentages, but are useful for the classification of the coins. With the coins divided into two groups according to adequate historical periods, we have obtained a correct classification (76-78%) based on the chemical compositions. Conclusions: X-ray fluorescent elemental analysis together with multivariate data analysis methods is suitable to group medieval coins according to historical periods. Keywords: X-ray fluorescence spectroscopy, Multivariate techniques, Coin, Silver, Middle age

Similarity and grouping of perlite and zeolite abrasive fillers. A replacement test

Inverse Gas Chromatography was used to estimate surface activity expressed by the dispersive component of the surface free energy, D S g , as well as parameters KA and KD describing surface ability to act as electron acceptor and donor, respectively. These parameters characterize the ability of the surface to specific interactions. The method was also applied to describe the magnitude of filler-phenolic resin interaction by Flory-Huggins parameter, . Granulation, surface area and porosity were also determined. The minimum number of parameters required to complete characterization of filler properties has been selected by principal component analysis. The usefulness of the selection for the abrasive industry has been proven. Moreover, the similarities and deviations from “an average” filler was determined by chemometric methods. Principal component analysis (PCA) and a novel procedure based on sum of ranking differences (SRD) were successfully applied for selection of the best fillers, and of advantageous parameters for characterization of the fillers. Similar and diverse fillers have been chosen based on clustering pattern by PCA and SRD

Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073

Supplementary material for: [https://doi.org/10.1016/j.chroma.2014.12.073]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/1650

Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006

Supplementary material for: [https://doi.org/10.1016/j.jpba.2015.07.006]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/1987

Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066

Supplementary material for: [https://doi.org/10.1016/j.chroma.2017.01.066]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/2428]Related to accepted version: [http://cherry.chem.bg.ac.rs/handle/123456789/3219

Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066

Supplementary material for: [https://doi.org/10.1016/j.chroma.2017.01.066]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/2428]Related to accepted version: [http://cherry.chem.bg.ac.rs/handle/123456789/3219

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters

<div><p>Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.</p></div

Apportionment and districting by Sum of Ranking Differences

Sum of Ranking Differences is an innovative statistical method that ranks competing solutions based on a reference point. The latter might arise naturally, or can be aggregated from the data. We provide two case studies to feature both possibilities. Apportionment and districting are two critical issues that emerge in relation to democratic elections. Theoreticians invented clever heuristics to measure malapportionment and the compactness of the shape of the constituencies, yet, there is no unique best method in either cases. Using data from Norway and the US we rank the standard methods both for the apportionment and for the districting problem. In case of apportionment, we find that all the classical methods perform reasonably well, with subtle but significant differences. By a small margin the Leximin method emerges as a winner, but—somewhat unexpectedly—the non-regular Imperiali method ties for first place. In districting, the Lee-Sallee index and a novel parametric method the so-called Moment Invariant performs the best, although the latter is sensitive to the function’s chosen parameter