A Review on the Use of Machine Learning for Pharmaceutical Formulations

The development of pharmaceutical formulations is a costly and uncertain process, which includes testing different combinations. We present a literature review on the use of Machine Learning (ML) techniques in the realm of pharmaceutical formulation. A systematic approach was carried out on the following scientific databases: PubMed, Springer Link, and IEEE Xplore. From evaluating 18 selected articles, this article presents and discusses the applications (e.g., Drug Delivery and Protein Development), ML techniques, the main contributions, and research challenges to be addressed. The results show a very promising scenario for ML, as the volume of scientific data is growing, as well as they reveal that there is no dominant or previously established solution for all cases. Those solutions must be used carefully and complementary to laboratory experimentation. We also identified that, despite the good results, there is still a need for advances in the processes of creation and use of complex and integrated databases.