Abstract
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.
基金项目
National Natural Science Foundation of China(22278070)
National Natural Science Foundation of China(21978047)
National Natural Science Foundation of China(21776046)
NETL
NSTL
维普
万方数据