A penalized quantitative structure–property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty. Issue 5 (4th May 2018)

• Record Type: Journal Article
• Authors: Al-Fakih, A. M.; Algamal, Z. Y.; Lee, M. H.; Aziz, M.
• Is Part Of: SAR and QSAR in environmental research. Volume 29:Issue 5(2018)
• Page Start: 339
• Page End: 353
• Publication Date: 2018-05-04
• Subjects: Penalized methods -- descriptor selection -- high-dimensional data -- energetic materials -- melting point -- QSPR; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2018.1439531
• Languages: English
• ISSNs: 1062-936X

Abstract

Abstract: A penalized quantitative structure–property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ( ) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ( ). The PBridge based model was internally and externally validated based on, , , , , , the Y-randomization test, , , , and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest of 0.959, of 0.953, of 0.949 and of 0.959, and the lowest and . For the test dataset, PBridge shows a higher of 0.945 and of 0.948, and a lower and, indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.