scholarly journals Comparison between Multi-Linear- and Radial-Basis-Function-Neural-Network-Based QSPR Models for The Prediction of The Critical Temperature, Critical Pressure and Acentric Factor of Organic Compounds

Molecules ◽  
2018 ◽  
Vol 23 (6) ◽  
pp. 1379 ◽  
Author(s):  
Mauro Banchero ◽  
Luigi Manna
Keyword(s):  
Neural Network ◽  
Critical Temperature ◽  
Radial Basis Function ◽  
Organic Compounds ◽  
Basis Function ◽  
Critical Pressure ◽  
Acentric Factor ◽  
Radial Basis

Radial basis function neural network-based QSPR for the prediction of critical temperature

2002 ◽  
Vol 62 (2) ◽  
pp. 217-225 ◽  
Author(s):  
Xiaojun Yao ◽  
Yawei Wang ◽  
Xiaoyun Zhang ◽  
Ruisheng Zhang ◽  
Mancang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Critical Temperature ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis

scholarly journals QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

2009 ◽  
Vol 7 (3) ◽  
pp. 439-445 ◽  
Author(s):  
Huitao Liu ◽  
Yingying Wen ◽  
Feng Luan ◽  
Yuan Gao ◽  
Xiuyong Li
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Organic Compounds ◽  
Basis Function ◽  
Heuristic Method ◽  
Reactivity Index ◽  
Structural Factors ◽  
Structure Property ◽  
Half Wave ◽  
Radial Basis

AbstractThe half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.

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