Novel Computational Approach by Combining Machine Learning with Molecular Thermodynamics for Predicting Drug Solubility in Solvents-Reference-Cited by-同舟云学术

Novel Computational Approach by Combining Machine Learning with Molecular Thermodynamics for Predicting Drug Solubility in Solvents

Published:2021-06-18 Issue:25 Volume:60 Page:9259-9268
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Ge Kai¹,Ji Yuanhui¹^ORCID

Affiliation:

1. Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, People’s Republic of China

Funder

Central University Basic Research Fund of China

Six Talent Peaks Project in Jiangsu Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c00998

Reference41 articles.

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

2. Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning

3. Improvement strategies for the oral bioavailability of poorly water-soluble flavonoids: An overview

4. A Review on Solubility Enhancement of Carvedilol—a BCS Class II Drug

5. Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering

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