Solubility prediction of anthracene in nonaqueous solvent mixtures using a combination of Jouyban-Acree and Abraham models

2006 ◽  
Vol 84 (6) ◽  
pp. 874-885
Author(s):  
Abolghasem Jouyban ◽  
Maryam Khoubnasabjafari ◽  
William E Acree, Jr.
Keyword(s):  
Data Sets ◽  
Nonaqueous Solvent ◽  
Solvent Mixtures ◽  
Structure Property ◽  
Solubility Prediction ◽  
Structure Property Relationships ◽  
Solvent Model ◽  
Quantitative Structure Property Relationships ◽  
Experimental Values ◽  
The Individual

The applicability of previously developed quantitative structure-property relationships was extended to predict the solubility of anthracene in nonaqueous binary and ternary solvent mixtures. The accuracy of the proposed methods was evaluated using 81 solubility data sets collected from the literature. The individual and mean percentage deviation (IPD and MPD) of experimental and computed solubilities were calculated as accuracy criteria. The computations were carried out using experimental and predicted mole fraction solubility of anthracene in monosolvent systems for binary and ternary solvent systems. The overall MPD of solubility prediction using experimental values in monosolvents varied from 5.2% to 4.2% and from 16.5% to 10.7% for binary and ternary solvents, using water to solvent and gas to solvent solvational parameters, respectively. The IPD distribution was better for the gas to solvent model. The corresponding ranges for the predicted solubility of anthracene in monosolvents were 47.9% to 28.1% and 23.9% to 22.5% for binary and ternary solvents, respectively, and IPD distribution was more favourable for the gas to solvent model. In general, the models derived from gas to solvent coefficients provided more accurate predictions and are recommended for practical applications.Key words: solubility, prediction, cosolvency, anthracene, Abraham model, Jouyban-Acree model.

QSPR STUDY OF RHEOLOGICAL AND MECHANICAL PROPERTIES OF CHLOROPRENE RUBBER ACCELERATORS

2014 ◽  
Vol 87 (2) ◽  
pp. 219-238 ◽  
Author(s):  
Roberto Todeschini ◽  
Viviana Consonni ◽  
Davide Ballabio ◽  
Andrea Mauri ◽  
Matteo Cassotti ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mathematical Models ◽  
Structural Features ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Cure Time ◽  
Chloroprene Rubber ◽  
Experimental Values ◽  
Rubber Accelerators

ABSTRACT In this preliminary study, mathematical models based on Quantitative Structure Property Relationships (QSPR) were applied in order to analyze how molecular structure of chloroprene rubber accelerators relates to their rheological and mechanical properties. QSPR models were developed in order to disclose which structural features mainly affect the mechanism of vulcanization. In such a way QSPR can help in a faster and more parsimonious design of new chloroprene rubber curative molecules. Regression mathematical models were calibrated on two rheological properties (scorch time and optimum cure time) and three mechanical properties (modulus 100%, hardness, and elongation at break). Models were calculated using experimental values of 14 accelerators belonging to diverse chemical classes and validated by means of different strategies. All the derived models gave a good degree of fitting (R2 values ranging from 84.5 to 98.7) and a satisfactory predictive power. Moreover, some hypotheses on the correlations between specific structural features and the analyzed rheological and mechanical properties were drawn. Owing to the relatively small set of accelerators used to calibrate the models, these hypotheses should be further investigated and proved.

scholarly journals Prediction of Parallel Artificial Membrane Permeability Assay of Some Drugs from their Theoretically Calculated Molecular Descriptors

2011 ◽  
Vol 8 (3) ◽  
pp. 1074-1085
Author(s):  
E. Konoz ◽  
Amir H. M. Sarrafi ◽  
S. Ardalani
Keyword(s):  
Neural Network ◽  
Membrane Permeability ◽  
Selection Procedure ◽  
Structure Property ◽  
Artificial Membrane ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Artificial Neural ◽  
Variable Subset Selection ◽  
Mlr Model

Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural network model in the prediction of membrane permeability of drugs.

scholarly journals A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

2021 ◽  
Author(s):  
Tobias Gensch ◽  
Gabriel dos Passos Gomes ◽  
Pascal Friederich ◽  
Ellyn Peters ◽  
Theophile Gaudin ◽  
...  
Keyword(s):  
Structure Property ◽  
Vast Number ◽  
Structure Property Relationships ◽  
Reaction Optimization ◽  
Physicochemical Descriptors ◽  
Mechanical Methods ◽  
Quantitative Structure Property Relationships ◽  
Property Space ◽  
Novel Catalyst ◽  
Molecular Catalysts

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce <i>kraken</i>, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of <i>kraken</i> to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization.

Characteristic study of irregularity measures of some nanotubes

2019 ◽  
Vol 97 (10) ◽  
pp. 1125-1132 ◽  
Author(s):  
Zahid Iqbal ◽  
Adnan Aslam ◽  
Muhammad Ishaq ◽  
Muhammad Aamir
Keyword(s):  
Quantitative Structure ◽  
Structure Property ◽  
Know How ◽  
Molecular Graphs ◽  
Irregularity Index ◽  
Structure Property Relationships ◽  
Boiling Points ◽  
Quantitative Structure Property Relationships ◽  
Quantitative Structure Activity Relationships ◽  
Vertex Degrees

In many applications and problems in material engineering and chemistry, it is valuable to know how irregular a given molecular structure is. Furthermore, measures of the irregularity of underlying molecular graphs could be helpful for quantitative structure property relationships and quantitative structure-activity relationships studies, and for determining and expressing chemical and physical properties, such as toxicity, resistance, and melting and boiling points. Here we explore the following three irregularity measures: the irregularity index by Albertson, the total irregularity, and the variance of vertex degrees. Using graph structural analysis and derivation, we compute the above-mentioned irregularity measures of several molecular graphs of nanotubes.

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