scholarly journals QSPR Models for the Molar Refraction, Polarizability and Refractive Index of Aliphatic Carboxylic Acids Using the ZEP Topological Index

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2359
Author(s):  
Zoiţa Mărioara Berinde
Keyword(s):  
Refractive Index ◽  
Carboxylic Acids ◽  
Topological Index ◽  
Molecular Descriptor ◽  
External Validation ◽  
Molar Refraction ◽  
Asymmetric Structure ◽  
Linear Regression Method ◽  
Structure Property ◽  
Statistical Tool

The molar refraction, polarizability, and refractive index for a series of monocarboxylic, dicarboxylic, and unsaturated monocarboxylic acids, having a symmetric or asymmetric structure, were investigated by the application of quantitative structure property relationship (QSPR) technique. We used a linear regression method and a single molecular descriptor, the ZEP topological index, calculated in a simple manner, with the help of weighted electronic distances, and also calculated on the basis of the chemical structure of the molecules. The high-quality performance and predictive ability of the QSPR models obtained were validated by means of specific validation techniques: y-randomization test, the leave-one-out cross validation procedure, and external validation. The investigated properties are well modeled (with r2 > 0.99) by the ZEP index, using the regression analysis as a statistical tool for developing reliable QSPR models. Our approach provides an alternative technique to the existing additive methods for predicting the molar refraction and polarizability of carboxylic acids, which is essentially based on the summation of atom and/or functional group contributions or bond contributions, and of some correction increments.

scholarly journals Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1101
Author(s):  
Elena M. Tosca ◽  
Roberta Bartolucci ◽  
Paolo Magni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Molecular Descriptor ◽  
Biological Properties ◽  
Training Dataset ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Qspr Model ◽  
Artificial Neural ◽  
Intrinsic Solubility

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

scholarly journals QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index

2014 ◽  
Vol 79 (8) ◽  
pp. 965-975 ◽  
Author(s):  
Long Jiao ◽  
Xiaofei Wang ◽  
LI. Hua ◽  
Yunxia Wang
Keyword(s):  
Partition Coefficient ◽  
Cross Validation ◽  
External Validation ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Prediction Ability ◽  
Vector Index ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
Edge Vector

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

Spectroscopic Properties of Rare Earth Ion Doped TeO2-B2O3-PbO Glass

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
A. Noranizah ◽  
K. Azman ◽  
H. Azhan ◽  
E. S. Nurbaisyatul ◽  
A. Mardhiah
Keyword(s):  
Refractive Index ◽  
Rare Earth ◽  
Glass Sample ◽  
Spectroscopic Properties ◽  
Molar Refraction ◽  
Energy Band Gap ◽  
Melt Quenching ◽  
Rare Earth Ion ◽  
Optical Energy Band Gap ◽  
Oxide Ion

This work focuses on the spectroscopic study of RE3+ ion, namely, trivalent neodymium (Nd3+) doped lead borotellurite glass with a composition of TeO2-B2O3-PbO. The glass sample has been prepared by conventional melt-quenching technique. The density, molar volume and optical energy band gap of these glasses have been measured. The refractive index, molar refraction and polarizability of oxide ion have been calculated by using Lorentz-Lorentz relations. The absorption spectra are recorded using UV-Vis-NIR spectrometer in the range of 200-900 nm.

scholarly journals Hosoya and Harary Polynomials of TOX(n),RTOX(n),TSL(n) and RTSL(n)

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Lian Chen ◽  
Abid Mehboob ◽  
Haseeb Ahmad ◽  
Waqas Nazeer ◽  
Muhammad Hussain ◽  
...  
Keyword(s):  
Topological Index ◽  
Molecular Descriptor ◽  
Wiener Index ◽  
Vital Role ◽  
Harary Index ◽  
Chemical Graph ◽  
Chemical Graph Theory ◽  
Hosoya Polynomial ◽  
Molecular Branching ◽  
Path Number

In the fields of chemical graph theory, topological index is a type of a molecular descriptor that is calculated based on the graph of a chemical compound. In 1947, Wiener introduced “path number” which is now known as Wiener index and is the oldest topological index related to molecular branching. Hosoya polynomial plays a vital role in determining Wiener index. In this report, we computed the Hosoya and the Harary polynomials for TOX(n),RTOX(n),TSL(n), and RTSL(n) networks. Moreover, we computed serval distance based topological indices, for example, Wiener index, Harary index, and multiplicative version of wiener index.

Prediction of glass transition temperatures of polyacrylates from the structures of motion units

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Xinliang Yu ◽  
Xianwei Huang
Keyword(s):  
Glass Transition ◽  
Molecular Descriptors ◽  
Amorphous Polymer ◽  
External Validation ◽  
Data Sets ◽  
Structure Property ◽  
Internal Validation ◽  
The Stability ◽  
Leave One Out ◽  
Mlr Model

The glass transition temperature [Formula: see text] is the most important parameter of an amorphous polymer. A quantitative structure-property relationship (QSPR) was developed for [Formula: see text]s of 82 polyacrylates, by applying stepwise multiple linear regression (MLR) analysis. Molecular descriptors used to describe polymer structures were, for the first time, calculated from the motion units of polymer backbones, which are chain segments with 20 carbons in length (10 repeating units). After internal validation with leave-one-out (LOO) method, external validation was carried out to test the stability of the MLR model of [Formula: see text]s. Compared to the models already published in the literature, the MLR model in this paper was accurate and acceptable, although our model was based on bigger data sets. The feasibility of calculating molecular descriptors from the motion units of polymer backbones for developing [Formula: see text] models of polyacrylates has been demonstrated.

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