AI-Driven Decision-Making Applications in Pharmaceutical Sciences
Bancha Yingngam, Abhiruj Navabhatra, Polpan Sillapapibool
Abstract
This chapter explores AI's influence on pharmaceutical sciences, highlighting its enhancement of traditional design methodologies. It explores AI's transformational role in key sectors, including drug discovery, virtual screening, and drug formulation development. AI's ability to efficiently identify potential drug candidates from large chemical libraries and its use of optimization algorithms in the selection of suitable excipients and dosage forms are discussed. The chapter also emphasizes AI's significance in improving pharmaceutical manufacturing processes through parameter refinement, quality outcome prediction, and real-time anomaly detection. The integration of traditional design methods with AI ensures robust, reliable, AI-driven processes that are compliant with regulations. In conclusion, the chapter highlights the potential of AI in pharmaceutical sciences and the importance of its integration with traditional design methods. This approach empowers scientists to innovate, speed up drug development, and improve patient outcomes.