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

doi:10.1016/j.cjche.2023.09.006

摘要/Abstract

摘要： The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients (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 between API and polymer successfully, which provided efficient guidance for the development of pharmaceutical formulations.

关键词: Multi-task machine learning, Density functional theory, Hydrogen bond interaction, Miscibility, Solubility

Abstract: The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients (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 between API and polymer successfully, which provided efficient guidance for the development of pharmaceutical formulations.

Key words: Multi-task machine learning, Density functional theory, Hydrogen bond interaction, Miscibility, Solubility

Kai Ge, Yiping Huang, Yuanhui Ji. Machine learning with active pharmaceutical ingredient/polymer interaction mechanism: Prediction for complex phase behaviors of pharmaceuticals and formulations[J]. 中国化学工程学报, 2024, 66(2): 263-272.

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