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The ever-increasing demand for novel drug development has spurred the adaptation of conventional research methods in the era of artificial intelligence. Pharmaceutical crystallization, as an essential part of drug development, has become a thrilling research frontier that reduces screening labor via integrating automated high-throughput platforms with in situ monitoring and data-driven algorithms (e.g., machine learning) to predict physicochemical properties and various solid-state forms. In this review, we started with a primer to introduce the machine learning algorithms that are widely used in pharmaceutical crystallization. Then, we systematically summarized recent advancements on high-throughput platforms to acquire huge amounts of data sets, prediction of physicochemical properties based on abundant experimental data, optimization and monitoring of pharmaceutical crystallization process to screen crystallization conditions, and prediction of polymorphs and cocrystals. Finally, we discussed the challenges and opportunities in an endeavor to develop a fully automated pharmaceutical crystallization screening paradigm for ultimately realizing a self-driving screening laboratory. This review highlights the frontier of artificial intelligence in pharmaceutical crystallization and offers a guideline for beginners to not only understand the basic principles of machine learning algorithms but also learn how to utilize machine learning to accelerate pharmaceutical crystallization development.
Zhu et al. (Fri,) studied this question.