Literature DB >> 1869898

Development of an automatic estimation system for both the partition coefficient and aqueous solubility.

T Suzuki1.   

Abstract

A computer program has been developed for estimating both the partition coefficient between 1-octanol and water phases and the aqueous solubility from the structural formula. This system is an extended version of a previously described program entitled CHEMICALC for the automatic estimation of the partition coefficient. The aqueous solubility is estimated via two pathways. The first is based on the linear relationship between logarithms of the aqueous solubilities of 497 compounds and their estimated 1-octanol/water partition coefficients. In the second, combined handling of two available group contribution methods of Irmann [Chem. Ing. Tech., 37 (1965) 789] and Wakita et al. [Chem. Pharm. Bull., 34 (1986) 4663] is adopted according to compound type. Some revisions and extensions of the methods for estimating the aqueous solubility have been made in both pathways, and the accuracy of the estimated aqueous solubilities for 497 compounds is discussed.

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Year:  1991        PMID: 1869898     DOI: 10.1007/bf00129753

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  1 in total

1.  Automatic log P estimation based on combined additive modeling methods.

Authors:  T Suzuki; Y Kudo
Journal:  J Comput Aided Mol Des       Date:  1990-06       Impact factor: 3.686

  1 in total
  4 in total

1.  In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

Authors:  Mario Lobell; Vinothini Sivarajah
Journal:  Mol Divers       Date:  2003       Impact factor: 2.943

2.  Classical QSAR and comparative molecular field analyses of the host-guest interaction of organic molecules with cyclodextrins.

Authors:  T Suzuki; M Ishida; W M Fabian
Journal:  J Comput Aided Mol Des       Date:  2000-10       Impact factor: 3.686

Review 3.  Machine learning for flow batteries: opportunities and challenges.

Authors:  Tianyu Li; Changkun Zhang; Xianfeng Li
Journal:  Chem Sci       Date:  2022-04-07       Impact factor: 9.969

Review 4.  QSPR studies on aqueous solubilities of drug-like compounds.

Authors:  Pablo R Duchowicz; Eduardo A Castro
Journal:  Int J Mol Sci       Date:  2009-06-03       Impact factor: 6.208

  4 in total

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