首页|Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

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The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical in-gredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms be-tween API and polymer successfully,which provided efficient guidance for the development of phar-maceutical formulations.

Multi-task machine learningDensity functional theoryHydrogen bond interactionMiscibilitySolubility

Kai Ge、Yiping Huang、Yuanhui Ji

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Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research,School of Chemistry and Chemical Engineering,Southeast University,Nanjing 211189,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

222780702197804721776046

2024

10.1016/j.cjche.2023.09.006

中国化学工程学报(英文版)
中国化工学会

中国化学工程学报(英文版)

CSTPCDEI
影响因子：0.818
ISSN：1004-9541
年,卷(期)：2024.66(2)
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