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Solubility; still a challenging subject in pharmaceutical sciences

Abolghasem Jouyban
2010 Revista Vitae  
The logP values could be computed using software such as ACD with reasonable accuracy. There is also a number of software which could be used to predict the aqueous solubility of drugs.  ...  Hildebrand (1881-1983 wrote: "There is scarcely anything more important for a chemist than a knowledge of solubilities, but unfortunately he finds it more difficult to predict how soluble a substance will  ...  The model requires experimental aqueous solubility data along with its logP as input data. The extended Hildebrand solubility approach of Prof.  ... 
doaj:972d5664ecea45678f1f3c6935b0457c fatcat:n3pnz6hfgffppiekzjqbaihgoq

Recent Advances on Aqueous Solubility Prediction

Junmei Wang, Tingjun Hou
2011 Combinatorial chemistry & high throughput screening  
Because of the importance of aqueous solubility, a lot of efforts have been spent on developing reliable models to predict this physiochemical property.  ...  The challenges of developing high quality aqueous solubility models as well as the strategies of surmounting those challenges have been discussed.  ...  The finding of the "Solubility Challenge" was recently published [49] .  ... 
doi:10.2174/138620711795508331 pmid:21470182 fatcat:zbtiksj7obflxmvpghk642gjua

Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?

Mario Lovrić, Kristina Pavlović, Petar Žuvela, Adrian Spataru, Bono Lučić, Roman Kern, Ming Wah Wong
2021 Journal of Chemometrics  
We present a collection of publicly available intrinsic aqueous solubility data of 829 drug-like compounds.  ...  prediction of solubility based on chemical structural information.  ...  Many other research groups also dealt with the solubility prediction challenge, attempting to predict both logS w (aqueous solubility; measured at a certain pH) and logS 0 (intrinsic solubility; solubility  ... 
doi:10.1002/cem.3349 fatcat:cxkciqcgg5azroc3kz7rt24qxe

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

Sergio Navarro Garcia, Xian Yang, Laura Bereczki, Dénes Kónya
2021 Molecules  
The kinetics of the transformation can give misleading solubility values according to Ostwald's rule.  ...  Compounds bearing central symmetry were shown to be about an order of magnitude less soluble in water than isomers without central symmetry. Counter ions also affected solubility.  ...  Acknowledgments: During the research Marvin was used for drawing, displaying, and characterizing. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/molecules26051203 pmid:33668137 pmcid:PMC7956567 fatcat:2mtiwgoicvhhfca3ipjm7dga3a

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

Michael A. Buonaiuto, Andrew S. I. D. Lang
2015 Chemistry Central Journal  
Conclusion: The 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure.  ...  Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.  ...  Acknowledgements The authors would like to acknowledge the contributors and judges of the Open Notebooks Science Challenge under the leadership of Jean-Claude Bradley without whom this work would not have  ... 
doi:10.1186/s13065-015-0131-2 pmid:26435734 pmcid:PMC4585410 fatcat:gp5vjnd2hndpnjt37jitr7jftm

Octanol-water partition coefficient measurements for the SAMPL6 Blind Prediction Challenge [article]

Mehtap Işık, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D Chodera
2019 bioRxiv   pre-print
The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa Prediction Challenge in a blind experimental benchmark  ...  The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based  ...  The SAMPL6 log P Prediction Challenge was 372 constructed only on prediction of neutral species. .  ... 
doi:10.1101/757393 fatcat:odu45gvr55acvg6nvkn6ty4qom

Crystal Engineering for Pharmaceutical Solids

Partha Pratim Bag
2020 Acta Scientific Pharmaceutical Sciences  
Pharmaceutical companies are extensively dedicated to conduct research for the development of the solubility of limited aqueous soluble drug molecules and their bioavailability.  ...  Continuously an intensive research in the pharmaceutical sciences has documented several approaches to address the issues of low aqueous solubility and bioavailability.  ... 
doi:10.31080/asps.2020.04.0498 fatcat:djt37ox3cve3hgix75scozix6u

Dielectric properties of water under extreme conditions and transport of carbonates in the deep Earth

D. Pan, L. Spanu, B. Harrison, D. A. Sverjensky, G. Galli
2013 Proceedings of the National Academy of Sciences of the United States of America  
We computed the dielectric constant of hot, compressed water using ab initio calculations (16, 17) with semilocal density functionals (18) and used our results to predict the solubility of carbonates in  ...  Using ab initio molecular dynamics, we computed the dielectric constant of water under the conditions of the Earth's upper mantle, and we predicted the solubility products of carbonate minerals.  ...  We used our DFT results to extend the revised HKF model of the free energy of aqueous species to upper mantle conditions in the Earth. We predicted the solubility products of carbonate minerals.  ... 
doi:10.1073/pnas.1221581110 pmid:23513225 pmcid:PMC3637742 fatcat:4guzviehe5b4bph5ln6rdibute

Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations [article]

Gihan Panapitiya, Michael Girard, Aaron Hollas, Vijay Murugesan, Wei Wang, Emily Saldanha
2021 arXiv   pre-print
The goal of this study is to develop a general model capable of predicting the solubility of a broad range of organic molecules.  ...  Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications.  ...  modeling approaches and molecular representations for the prediction of aqueous solubility using the largest set of solubility measurements to date.  ... 
arXiv:2105.12638v2 fatcat:hdsopkrpbvbcxdnexvgmyiuny4

Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

Alessandro Lusci, Gianluca Pollastri, Pierre Baldi
2013 Journal of Chemical Information and Modeling  
Several variants of this approach are applied to the problem of predicting aqueous solubility and tested on four benchmark datasets.  ...  Here we present a brief overview of deep learning methods and show in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties.  ...  We wish to acknowledge OpenEye Scientific Software and ChemAxon for academic software licenses, and Jordan Hayes and Yuzo Kanomata for computing support.  ... 
doi:10.1021/ci400187y pmid:23795551 pmcid:PMC3739985 fatcat:ftneps7ijrf6zpm7qhrcplcroe

Solubility prediction in the bRo5 chemical space: where are we right now?

Giuseppe Ermondi, Vasanthanathan Poongavanam, Maura Vallaro, Jan Kihlberg, Giulia Caron
2020 ADMET and DMPK  
Currently, methods for modelling of solubility will have to be tailored to the set of investigated compounds.  ...  Modelling the solubility of compounds in the "beyond Rule of 5" (bRo5) chemical space is in its infancy and to date only a few studies have been reported in the literature.  ...  Acknowledgements: Yuriy Abramov and Gilles Goetz are kindly acknowledged for sharing their expertise in the field.  ... 
doi:10.5599/admet.834 pmid:35300306 pmcid:PMC8915608 doaj:0c47ac79bec0483bac88824e0014f385 fatcat:qn7tmjrpfbcr7iac3e2w34aebu

ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches

Gabriela Falcón-Cano, Christophe Molina, Miguel Angel Cabrera-Pérez
2020 ADMET and DMPK  
In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes.  ...  However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors  ...  Predictive models for aqueous solubility In order to find a way to improve the predictive accuracy of aqueous solubility models in silico, a new protocol was developed based on the combination of regression  ... 
doi:10.5599/admet.852 pmid:35300309 pmcid:PMC8915604 fatcat:e32vx3odpnagjc7vwdolb2gt7y

Solubility of pioglitazone hydrochloride in ethanol, N-methyl pyrrolidone, polyethylene glycols and water mixtures at 298.20 °K

Sh Soltanpour, M Barzegar-Jalali, A Jouyban
2011 DARU  
The solubility of PGZ-HCl was increased by addition of EtOH, NMP, PEGs 200, 400 and 600 to aqueous solutions.  ...  Also a previously proposed version of the model was used to predict the solubility of PGZ-HCl in binary and ternary mixtures employing the experimental solubility data in mono-solvents.  ...  To provide more accurate predictions, it is possible to include ternary solvent interaction terms to the model, but it requires more experimental efforts, i.e. measurement of a number of solubility data  ... 
pmid:23008690 pmcid:PMC3436081 fatcat:xi5zzey5krhylfof5x3nl37awi

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Samuel Boobier, David R. J. Hose, A. John Blacker, Bao N. Nguyen
2020 Nature Communications  
Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.  ...  Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method  ...  Acknowledgements This work was undertaken on ARC2, ARC3 and ARC4, part of the High Performance Computing facilities at the University of Leeds, UK.  ... 
doi:10.1038/s41467-020-19594-z pmid:33188226 fatcat:dhbryl6ckfao5dycuw3fctlprm

TopP-S: Persistent homology based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility [article]

Kedi Wu, Zhixiong Zhao, Renxiao Wang, Guo-Wei Wei
2017 arXiv   pre-print
It is demonstrate that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at  ...  Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery.  ...  a high variability brings challenge to solubility prediction.  ... 
arXiv:1801.01558v1 fatcat:cqidbn4c4rcd5hwaetkafbjeie
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