(untitled) (Computer Aided Drug Development, 2024)
Author Mr. Gourab Saha Assistant Professor, Department of Pharmaceutics, College of Pharmaceutical Sciences, Berhampur, Mohada, Odisha, India Abstract Artificial intelligence has transformed pharmaceutical formulation development through advanced computational methods and predictive modeling. Machine learning algorithms analyze vast formulation databases to identify optimal excipient combinations and processing parameters. Deep learning networks predict drug-excipient compatibility, stability profiles, and dissolution characteristics with unprecedented accuracy. AI-powered systems accelerate preformulation studies by predicting physicochemical properties and potential formulation challenges. Neural networks optimize formulation compositions by analyzing multiple variables simultaneously, reducing experimental iterations. Molecular modeling integrated with AI enhances understanding of drug-excipient interactions at the molecular level. Quality by Design (QbD) principles are implemented through AI-driven process optimization and control strategies. Automated formulation design platforms incorporate historical data and scientific literature to generate novel formulation approaches. Knowledge management systems capture and utilize formulation expertise across organizations. Risk assessment models powered by AI identify potential formulation and process risks. These technological advances significantly reduce development time, optimize resource utilization, and improve formulation success rates while ensuring consistent product quality. Keywords: Machine Learning; Formulation Optimization; Predictive Modeling; Process Control; Quality by Design; Neural Networks View PDF