Literature DB >> 18624401

Solubility challenge: can you predict solubilities of 32 molecules using a database of 100 reliable measurements?

Antonio Llinàs1, Robert C Glen, Jonathan M Goodman.   

Abstract

Solubility is a key physicochemical property of molecules. Serious deficiencies exist in the consistency and reliability of solubility data in the literature. The accurate prediction of solubility would be very useful. However, systematic errors and lack of metadata associated with measurements greatly reduce the confidence in current models. To address this, we are accurately measuring intrinsic solubility values, and here we report results for a diverse set of 100 druglike molecules at 25 degrees C and an ionic strength of 0.15 M using the CheqSol approach. This is a highly reproducible potentiometric technique that ensures the thermodynamic equilibrium is reached rapidly. Results with a coefficient of variation higher than 4% were rejected. In addition, the Potentiometric Cycling for Polymorph Creation method, [PC] (2), was used to obtain multiple polymorph forms from aqueous solution. We now challenge researchers to predict the intrinsic solubility of 32 other druglike molecules that have been measured but are yet to be published.

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Year:  2008        PMID: 18624401     DOI: 10.1021/ci800058v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  27 in total

1.  Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations.

Authors:  Pavel V Klimovich; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2010-04-06       Impact factor: 3.686

2.  Predicting protein-ligand affinity with a random matrix framework.

Authors:  Alpha A Lee; Michael P Brenner; Lucy J Colwell
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-16       Impact factor: 11.205

3.  SAMPL4, a blind challenge for computational solvation free energies: the compounds considered.

Authors:  J Peter Guthrie
Journal:  J Comput Aided Mol Des       Date:  2014-04-06       Impact factor: 3.686

4.  Cocrystal Solubility Product Prediction Using an in combo Model and Simulations to Improve Design of Experiments.

Authors:  Alex Avdeef
Journal:  Pharm Res       Date:  2018-02-02       Impact factor: 4.200

5.  Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research.

Authors:  Denis Fourches; Eugene Muratov; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2010-07-26       Impact factor: 4.956

6.  Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

Authors:  Alessandro Lusci; Gianluca Pollastri; Pierre Baldi
Journal:  J Chem Inf Model       Date:  2013-07-02       Impact factor: 4.956

7.  Accurate Physical Property Predictions via Deep Learning.

Authors:  Yuanyuan Hou; Shiyu Wang; Bing Bai; H C Stephen Chan; Shuguang Yuan
Journal:  Molecules       Date:  2022-03-03       Impact factor: 4.411

8.  Accuracy of continuum electrostatic calculations based on three common dielectric boundary definitions.

Authors:  Alexey V Onufriev; Boris Aguilar
Journal:  J Theor Comput Chem       Date:  2014-05       Impact factor: 0.939

9.  Aqueous Solubility of Organic Compounds for Flow Battery Applications: Symmetry and Counter Ion Design to Avoid Low-Solubility Polymorphs.

Authors:  Sergio Navarro Garcia; Xian Yang; Laura Bereczki; Dénes Kónya
Journal:  Molecules       Date:  2021-02-24       Impact factor: 4.411

10.  Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

Authors:  Tiejun Cheng; Qingliang Li; Yanli Wang; Stephen H Bryant
Journal:  J Chem Inf Model       Date:  2011-01-07       Impact factor: 4.956

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