Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization-Reference-Cited by-同舟云学术

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

Published:2021-10-13 Issue: Volume: Page:
ISSN:2095-0179
Container-title:Frontiers of Chemical Science and Engineering
language:en
Short-container-title:Front. Chem. Sci. Eng.

Author:

Ma Yiming,Gao Zhenguo,Shi Peng,Chen Mingyang,Wu Songgu,Yang Chao,Wang Jingkang,Cheng Jingcai,Gong Junbo

Publisher

Springer Science and Business Media LLC

Subject

General Chemical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11705-021-2083-5.pdf

Reference52 articles.

1. Ma H, Qu Y, Zhou Z, Wang S, Li L. Solubility of thiotriazinone in binary solvent mixtures of water + methanol and water + ethanol from (283 to 330) K. Journal of Chemical & Engineering Data, 2012, 57(8): 2121–2127

2. Maher A, Rasmuson A, Croker D, Hodnett B. Solubility of the metastable polymorph of piracetam (Form II) in a range of solvents. Journal of Chemical & Engineering Data, 2012, 57(12): 3525–3531

3. Ma Y, Wu S, Macaringue E, Zhang T, Gong J, Wang J. Recent progress in continuous crystallization of pharmaceutical products: precise preparation and control. Organic Process Research & Development, 2020, 24(10): 1785–1801

4. Wang Y, Du S, Wu S, Li L, Zhang D, Yu B, Zhou L, Bekele H, Gong J. Thermodynamic and molecular investigation into the solubility, stability and self-assembly of gabapentin anhydrate and hydrate. Journal of Chemical Thermodynamics, 2017, 113: 132–143

5. Wang X, Zhang D, Liu S, Chen Y, Jia L, Wu S. Thermodynamic study of solubility for imatinib mesylate in nine monosolvents and two binary solvent mixtures from 278.15 to 318.15 K. Journal of Chemical & Engineering Data, 2018, 63(11): 4114–4127

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1. Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures;Industrial & Engineering Chemistry Research;2024-09-11

2. A hybrid approach to aqueous solubility prediction using COSMO-RS and machine learning;Chemical Engineering Research and Design;2024-09

3. Artificial Intelligence Assisted Pharmaceutical Crystallization;Crystal Growth & Design;2024-05-03

4. ANFIS-Driven Machine Learning Automated Platform for Cooling Crystallization Process Development;Organic Process Research & Development;2024-04-04

5. Vapor-liquid phase equilibria behavior prediction of binary mixtures using machine learning;Chemical Engineering Science;2023-12

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