Quantitative structure–property relationships and neural networks: correlation and prediction of physical properties of pure components and mixtures from molecular structure-Reference-Cited by-同舟云学术

Quantitative structure–property relationships and neural networks: correlation and prediction of physical properties of pure components and mixtures from molecular structure

Published:1999-06 Issue: Volume:158-160 Page:367-374
ISSN:0378-3812
Container-title:Fluid Phase Equilibria
language:en
Short-container-title:Fluid Phase Equilibria

Author:

Bünz A.P,Braun B,Janowsky R

Publisher

Elsevier BV

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy,General Chemical Engineering

Reference6 articles.

1. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure

2. Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships

3. Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques

4. Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies

5. L.B. Kier, L.H. Hall, Molecular Connectivity in Chemistry and Drug Research, Academic Press, New York, 1976.

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SGC based prediction of the flash point temperature of pure compounds;Journal of Loss Prevention in the Process Industries;2018-07

2. Prediction of the Upper Flammability Limit of Pure Compounds;Asian Journal of Applied Science and Engineering;2016-03-26

3. Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks;Journal of the Taiwan Institute of Chemical Engineers;2016-03

4. A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion;Journal of Chemical & Engineering Data;2015-11-18

5. MNLR and ANN structural group contribution methods for predicting the flash point temperature of pure compounds in the transportation fuels range;Process Safety and Environmental Protection;2015-01