Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review

Abstract

Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex and time-consuming process of drug development, many firms, including big pharma and medium-scale industries, are constantly looking for ways to reduce the time for providing lifesaving medications to patients in need without compromising the safety and efficacy of the product. Formulation of novel drug products in a pharmaceutical R&D and scaling up the process to a large-scale production involves a huge investment and an eye for detail in the intricacies of the processes.

Intervention of AI and machine learning (ML) can solve many problems in this aspect. With the rise of Industry 4.0, the relative shift of industry towards process automation, accelerated development has become vital in all domains. The investments in R&D by the large pharmaceutical companies reached up to $190 bn in 2024, according to a report by IQVIA. There is a noted upsurge in investments in the domains interlinking AI and ML with pharmaceutical research. Pharmaceutical formulation development can excel in the early stages, and the productivity can witness a steady growth if AI and ML tools are utilized.

Most of the research in this domain remains in the budding stages, and its adoption in the industry needs further refinement by delineating structured guidance from the experts and regulatory agencies.  The current review speaks about the current studies reported in the arena of formulation development and also sheds light on some of the areas where the pharmaceutical product development on a larger scale can benefit from AI and ML.

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Warke, S., Katari, O. & Jain, S. Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review. AAPS PharmSciTech 27, 44 (2026). https://doi.org/10.1208/s12249-025-03296-0

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