scholarly journals QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

2010 ◽  
Vol 4 ◽  
pp. EHI.S5168 ◽  
Author(s):  
Omar Deeb ◽  
Padmakar V. Khadikar ◽  
Mohammad Goodarzi
Keyword(s):  
Organic Compounds ◽  
Bioconcentration Factors ◽  
Qspr Modeling

QSPR Modeling of Gibbs Free Energy of Organic Compounds by Weighting of Nearest Neighboring Codes

2005 ◽  
Vol 16 (3) ◽  
pp. 305-324 ◽  
Author(s):  
E. A. Castro ◽  
A. P. Toropova ◽  
A. A. Toropov ◽  
D. V. Mukhamedjanova
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
Organic Compounds ◽  
Qspr Modeling

Using HPLC Retention Parameters to Estimate Fish Bioconcentration Factors of Organic Compounds

2004 ◽  
Vol 27 (12) ◽  
pp. 1861-1873 ◽  
Author(s):  
Rongbo Guo ◽  
Xinmiao Liang ◽  
Jiping Chen ◽  
Qing Zhang ◽  
Wenzhong Wu ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Bioconcentration Factors ◽  
Retention Parameters

QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors

2019 ◽  
Vol 4 (4) ◽  
pp. 15-26
Author(s):  
Khalid Bouhedjar ◽  
Abdelmalek Khorief Nacereddine ◽  
Hamida Ghorab ◽  
Abdelhafid Djerourou
Keyword(s):  
Organic Compounds ◽  
High Speed ◽  
External Validation ◽  
Model Performance ◽  
Predictive Ability ◽  
Structure Property ◽  
Critical Temperatures ◽  
Quantitative Structure Property Relationships ◽  
Qspr Modeling ◽  
External Test

The simplified molecular input line entry system (SMILES) is particularly suitable for high-speed machine processing, based on the Monte Carlo method using CORAL software. Quantitative structure-property relationships (QSPR) of critical temperatures have been established using a dataset of 165 diverse organic compounds employing hybrid optimal descriptors defined by graph and SMILES notation. External validation is one of the most important parts in the evaluation of model performance. However, previous models on the same dataset have poor predictive power in the external test set, or the authors had not done that check. In the present work, the predictive ability of model has been tested using external validation. The statistical quality of the three splits are similar and good. The r2 values for the best model are: r2 = 0.98 for the training set, r2 = 0.95 for the calibration set, and r2 = 0.94 for the validation set.

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