Revolutionizing drug formulation development: The increasing impact of machine learning.

Area of Science:

• Pharmaceutical Science
• Computational Chemistry
• Biotechnology

Background:

• Formulation science is critical for drug delivery, as shown by mRNA vaccines.
• Traditional formulation development relies on extensive, costly wet-lab experiments.
• Machine learning (ML) offers a promising approach to accelerate this process.

Purpose of the Study:

• To provide an updated review of ML applications in drug formulation development.
• To introduce advanced ML techniques used in formulation design.
• To discuss the progress towards self-driving laboratories and future ML applications.

Main Methods:

• Literature review of recent advancements in ML for formulation science.
• Analysis of emerging ML techniques applied to drug design.
• Discussion of progress in automated laboratory systems.

Main Results:

• Significant growth in ML applications for formulation development since 2021.
• Implementation of advanced ML techniques for enhanced formulation design.
• Progress in developing self-driving laboratories for accelerated research.

Conclusions:

• ML is rapidly transforming drug formulation development.
• Advanced ML techniques and automated labs are key to future innovation.
• Further exploitation of ML holds significant potential for pharmaceutical R&D.