Literature DB >> 30058484

Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict?

Oleg A Raevsky1, Veniamin Y Grigorev1, Daniel E Polianczyk1, Olga E Raevskaja1, John C Dearden2.   

Abstract

Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Keywords:  ADMET; Aqueous solubility; QSPR; methods and models; molecular descriptors; thermodynamic.

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Year:  2019        PMID： 30058484     DOI： 10.2174/1389557518666180727164417

Source DB:  PubMed          Journal:  Mini Rev Med Chem        ISSN： 1389-5575            Impact factor:   3.862


  3 in total

1.  Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.

Authors:  Chenglong Deng; Li Liang; Guomeng Xing; Yi Hua; Tao Lu; Yanmin Zhang; Yadong Chen; Haichun Liu
Journal:  Mol Divers       Date:  2022-06-23       Impact factor: 2.943

2.  Pushing the limits of solubility prediction via quality-oriented data selection.

Authors:  Murat Cihan Sorkun; J M Vianney A Koelman; Süleyman Er
Journal:  iScience       Date:  2020-12-17

3.  ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches.

Authors:  Gabriela Falcón-Cano; Christophe Molina; Miguel Ángel Cabrera-Pérez
Journal:  ADMET DMPK       Date:  2020-08-07
  3 in total

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