Abstract

The integration of artificial intelligence (AI) in drug discovery is transforming the pharmaceutical landscape, particularly in the realms of pharmacokinetics (PK) and pharmacodynamics (PD). This paper explores how AI-driven methodologies enhance the understanding of drug absorption, distribution, metabolism, and excretion, while also optimizing the therapeutic efficacy and safety profiles of new compounds. By leveraging machine learning algorithms and big data analytics, researchers can identify novel drug candidates more efficiently and predict their interactions with biological systems. The implications of AI on clinical trials, dosage optimization, and personalized medicine are discussed, highlighting its potential to streamline the drug development process and improve patient outcomes. This review emphasizes the necessity of interdisciplinary collaboration in harnessing AI's capabilities to address the complexities of drug behavior in clinical settings