Quantitative Structure−Property Relationship (QSPR) for the Adsorption of Organic Compounds onto Activated Carbon Cloth: Comparison between Multiple Linear Regression and Neural Network

1999 ◽  
Vol 33 (23) ◽  
pp. 4226-4231 ◽  
Author(s):  
C. Brasquet ◽  
B. Bourges ◽  
P. Le Cloirec
Keyword(s):  
Neural Network ◽  
Activated Carbon ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Organic Compounds ◽  
Carbon Cloth ◽  
Quantitative Structure ◽  
Structure Property ◽  
Activated Carbon Cloth ◽  
Structure Property Relationship

Quantitative structure-property relationship study of alcohols water solubility based on a new model combined modified autocorrelation method and electro-topological indices

2020 ◽  
Author(s):  
Chaymae Jermouni ◽  
Mohamed Nohair
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Solubility ◽  
Topological Descriptors ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationship ◽  
Autocorrelation Method

Abstract In this study, structure water solubility modeling was performed to describe a set of 50 of aliphatic alcohols in a Quantitative Structure-Property Relationship model by developing of two descriptors types based on multifunctional autocorrelation method, which gives a general description of whole molecule; and electro-topological descriptors. The index combines the topological nature with electronic state of the atom. The Modified Autocorrelation Method was used in structure–property relationships to describe the local environment of the hydroxyl group. For the statistical studies, Multiple Linear Regression, Artificial Neural Networks and Principal Components Analysis were used. The approach efficiency approach was evaluated through the predictive ability of models by leave-p-Out cross-validation method. The coefficient of determination and errors of descriptors combination calculated respectively by multiple linear regression and artificial neural networks were r= 0.99, s = 0.18 and r = 0.99, s = 0.32. In order to simplify components computation, the molecules were coded by means of SMILES system and stored as input files. The results showed that aliphatic alcohols solubility is dominated by the shape and molecule branching, also the electro-topological descriptors had a considered model effect.

Quantitative Structure-Property Relationship (QSPR) Study on the Complex Compounds Formed From Gadolinium(III) with Dibutyl Dithiophosphate Derivatives Ligands Based on Molecular Descriptors

2021 ◽  
Vol 874 ◽  
pp. 171-181
Author(s):  
Nurdeni ◽  
Atje Setiawan Abdullah ◽  
Budi Nurani Ruchjana ◽  
Anni Anggraeni ◽  
Annisa Nur Falah ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Bond Length ◽  
Bond Angle ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Complex Compounds ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationship

A study of the quantitative relationship of structure and property (Quantitative Structure Property Relationship (QSPR) has been carried out on complex compounds formed between gadolinium (Gd) and dibutyldithiophosphate (DBDTP) derivative ligands. This study is a part of our laboratory research program on the development of extractant ligands, including DBDTP in extraction for the separation and purification of rare-earth elements (REEs), specifically Gd. Gadolinium has also been a part of the research program about its use in the synthesis of magnetic resonance imaging (MRI) contrast agents, for the diagnosis of various diseases. This chemical calculation research aims to analyze the effect of descriptors in the form of parameters of the physical-chemical properties of bond lengths, bond angles, and bond energies on the stability of Gd complex compounds with DBDTP derivative ligands. To get descriptors PM7 semi-empirical method was used, while for data analysis, Multiple Linear Regression Analysis was used, assuming the model error is normally distributed with zero mean and constant variance. Furthermore, data processing was done using SPSS software. This research was conducted by involving 28 DBDTP derivative ligands and using multiple linear regression analysis. The regression equation is Y ̂ = - 0.966 + 0.586 V1 - 0.014 V2 + 0.000 V3. From the resulted research data it was found that there are three findings, namely: (1) bond length and bond angle have a significant simultaneous effect on stability of Gd complex compounds with DBDTP derivative ligands; (2) bond length and bond angle have a partially significant effect on stability of Gd complex compounds with DBDTP derivative ligands; (3) bond length proved to have a significant dominant effect on stability of Gd complex compounds with DBDTP derivative ligands.

Sign in / Sign up

Export Citation Format

Share Document