  Current pharmaceutical formulation development still strongly relies on the
traditional trial-and-error approach by individual experiences of
pharmaceutical scientists, which 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
this research is to use deep learning to predict pharmaceutical formulations.
In this paper, two different types of dosage forms were chosen as model
systems. Evaluation criteria suitable for pharmaceutics were applied to
assessing 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 with
deep learning. The result shows the accuracies of both two deep neural networks
were above 80% and higher than other machine learning models, which showed good
prediction in pharmaceutical formulations. In summary, deep learning with the
automatic data splitting algorithm and the evaluation criteria suitable for
pharmaceutical formulation data was firstly developed for the prediction of
pharmaceutical formulations. The cross-disciplinary integration of
pharmaceutics and artificial intelligence may shift the paradigm of
pharmaceutical researches from experience-dependent studies to data-driven
methodologies.
