Literature DB >> 17654337

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 2. Solubility in 1-octanol.

O A Raevsky1, G L Perlovich, K-J Schaper.   

Abstract

On the basis of octanol solubility data (log S(o)) for 218 structurally diverse solid chemicals it was shown that the exclusive consideration of melting points did not provide satisfactory results in the quantitative prediction of this parameter (s = 0.92). The application of HYBOT physicochemical descriptors separately (s = 0.94) and together with melting points (s = 0.70) in the framework of a common regression model also was not successful, although contributions of volume-related and H-bond terms to solubility in octanol were identified. It was proposed that the main reason for such behaviour was the different crystal lattice interaction of different classes of chemicals. Successful calculations of the solubility in octanol of chemicals of interest were performed on the basis of the experimental solubility of structurally/physicochemically/numerically similar nearest neighbours with consideration of their difference in physicochemical parameters (molecular polarisability, H-bond acceptor and donor factors (s = 0.66)) and of these descriptors together with melting point differences (s = 0.38). Good results were obtained for all compounds having nearest neighbours with sufficient similarity, expressed by Tanimoto indexes, and by distances in the scaled 3D descriptor space. Obviously the success of this approach depends on the size of the database.

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Year:  2007        PMID: 17654337     DOI: 10.1080/10629360701430124

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  3 in total

1.  Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge.

Authors:  Michael A Buonaiuto; Andrew S I D Lang
Journal:  Chem Cent J       Date:  2015-09-24       Impact factor: 4.215

2.  Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms.

Authors:  Zhuyifan Ye; Defang Ouyang
Journal:  J Cheminform       Date:  2021-12-11       Impact factor: 5.514

3.  Descriptors for the Prediction of Partition Coefficients and Solubilities of Organophosphorus Compounds.

Authors:  Michael H Abraham; William E Acree
Journal:  Sep Sci Technol       Date:  2013-03-14       Impact factor: 2.475

  3 in total

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