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

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

Khalid Bouhedjar, Abdelmalek Khorief Nacereddine, Hamida Ghorab, Abdelhafid Djerourou
Copyright: © 2019 |Volume: 4 |Issue: 4 |Pages: 12
ISSN: 2379-7487|EISSN: 2379-7479|EISBN13: 9781522570554|DOI: 10.4018/IJQSPR.2019100102
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MLA

Bouhedjar, Khalid, et al. "QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors." IJQSPR vol.4, no.4 2019: pp.15-26. http://doi.org/10.4018/IJQSPR.2019100102

APA

Bouhedjar, K., Nacereddine, A. K., Ghorab, H., & Djerourou, A. (2019). QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 4(4), 15-26. http://doi.org/10.4018/IJQSPR.2019100102

Chicago

Bouhedjar, Khalid, et al. "QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors," International Journal of Quantitative Structure-Property Relationships (IJQSPR) 4, no.4: 15-26. http://doi.org/10.4018/IJQSPR.2019100102

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Abstract

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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