This research explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in the pharmaceutical sector, specifically focusing on drug discovery. Our objectives are twofold: firstly, to evaluate the advantages, limitations, and challenges posed by AI in drug discovery; and secondly, to propose comprehensive strategies for addressing these challenges. To meet these objectives, we conducted a thorough review of existing literature, emphasizing AI applications, notably deep learning, within pharmaceutical research. We also explored various aspects, such as Quantitative Structure-Activity Relationship/Quantitative Structure- Property Relationship (QSAR/QSPR) modeling, de novo drug design, and chemical synthesis prediction. Our approach involved case studies and large-scale applications, extracting insights from diverse sources. Our findings illustrate how AI can revolutionize drug development, enhance drug design, and refine drug screening. However, we acknowledge the persistent challenges related to data availability and ethical considerations, requiring careful attention to harness AI's full potential in pharmaceutical research. Our study underscores AI's growing impact on the pharmaceutical industry, offering promising avenues for increased research efficiency and potentially life- saving discoveries. By addressing data and ethical concerns, we believe that AI can pave the way for groundbreaking advancements in pharmaceutical research. This paper provides an in-depth overview of AI's current state in pharmaceutical research and a comprehensive framework for navigating this critical domain.

Keywords : Drug Discovery, Artificial Intelligence, QSAR/QSPR Modeling, Limitations, Pharmaceuticals, Machine Learning, Data Challenges, and Ethical Considerations.