Deep learning for in vitro prediction of pharmaceutical formulations.

ABSTRACT: Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared w

SUBMITTER: Yang Y 

PROVIDER: S-EPMC6362259 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature