Authors: Ms. Tanvi Parab, Ms.Saloni Pawar, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakkar
Abstract: The field of drug discovery has undergone a remarkable transformation with the integration of artificial intelligence (AI) techniques. AI-driven approaches have the potential to significantly accelerate and enhance the drug discovery pipeline by streamlining key stages such as target identification, compound screening, lead optimization, and preclinical prediction. This paper provides a comprehensive overview of the various AI methodologies employed in drug discovery, including machine learning, deep learning, reinforcement learning, and natural language processing. We explore how these technologies are being utilized to analyze complex biological data, predict molecular interactions, and identify promising drug candidates with greater efficiency and accuracy. Furthermore, the paper examines the challenges and limitations associated with data quality, model interpretability, and regulatory acceptance. We also highlight recent advancements and successful case studies demonstrating real-world applications of AI in pharmaceutical research. Ethical implications, data privacy concerns, and the evolving role of human expertise in AI- assisted workflows are critically discussed. Finally, the paper outlines future prospects, emphasizing the potential of AI to revolutionize personalized medicine and accelerate the development of novel therapeutics in a cost-effective and time- efficient manner.
