Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures-Reference-Cited by-同舟云学术

Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures

Published:2024-09-11 Issue: Volume: Page:
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhao Yang¹,Li Wei²,Zhao Chenyang³,Fang Lan⁴,Yang Huaiyu²^ORCID,Ma Yiming²^ORCID

Affiliation:

1. SINOPEC Research Institute of Petroleum Processing Company, Ltd., Beijing 100083, People’s Republic of China

2. Department of Chemical Engineering, Loughborough University, Leicestershire LE113TU, United Kingdom

3. School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, People’s Republic of China

4. College of Chemical and Biological Engineering, Shandong University of Science and Technology, Qingdao 266590, People’s Republic of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c02110

Reference41 articles.

1. Thermodynamic analysis and molecular dynamic simulation of the solubility of vortioxetine hydrobromide in three binary solvent mixtures

2. High throughput solubility measurement in drug discovery and development

3. Predicting the Solvation of Organic Compounds in Aqueous Environments: From Alkanes and Alcohols to Pharmaceuticals

4. Bridging Machine Learning and Redlich–Kister Theory for Solid–Liquid Equilibria Prediction of Binary Eutectic Solvent Systems

5. Solubility Prediction of Pharmaceutical and Chemical Compounds in Pure and Mixed Solvents Using Predictive Models