Deep learning for in vitro prediction of pharmaceutical formulations

TL;DR

This paper demonstrates that deep learning models can accurately predict pharmaceutical formulations, outperforming traditional machine learning methods, and introduces an automatic dataset selection algorithm to enhance prediction reliability.

Contribution

It is the first to develop deep learning models with an automatic data splitting algorithm for pharmaceutical formulation prediction.

Findings

Deep neural networks achieved over 80% accuracy.

Deep learning outperformed six other machine learning methods.

The approach enables data-driven pharmaceutical research.

Abstract

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.