DOI: https://doi.org/10.63345/ijrmp.v12.i6.3
Dinesh Lepcha
Independent Researcher
Sikkim, India
Abstract
The rising complexity of drug formulation has created a demand for innovative approaches that integrate artificial intelligence (AI) with pharmaceutical sciences. This manuscript explores the development and implementation of AI-powered algorithms tailored to disease-specific drug formulation. By leveraging machine learning techniques, data mining, and predictive analytics, the study aims to optimize the drug development process, minimize trial-and-error experimentation, and provide personalized therapeutic interventions. The research outlines the conceptual framework of the algorithm, reviews pertinent literature up to 2022, details the methodology implemented in our experimental design, and presents significant findings that underscore the potential of AI in revolutionizing drug formulation. The results indicate improved prediction accuracy for optimal formulations, decreased formulation time, and enhanced safety profiles. Conclusively, the study addresses the challenges and opportunities in integrating AI into drug development and outlines future directions for research and industrial applications.
Keywords
Artificial Intelligence; Machine Learning; Drug Formulation; Disease-Specific; Predictive Analytics; Pharmaceutical Development