AI-Enabled Models in the Restoration of Drug Efficacy and Drug Design

The chapter “AI-Based Models in Drug Discovery and Drug Design” delves into the profound and dynamic integration of artificial intelligence (AI) within the domain of pharmaceutical research. The Conventional drug discovery process, historically fraught with high costs and lengthy timelines, has undergone a remarkable transformation, primarily driven by AI technologies. The narrative begins by elucidating the critical importance of data collection and management, where AI-driven algorithms and data analytics have become indispensable tool for extracting valuable insights from the vast repositories of biomedical information. This newfound intelligence extends to target identification and validation, where AL models excel at identifying potential drug targets with exceptional precision, significantly expediting the early stages of drug discovery. AI's influence permeates further, empowering molecular modeling and in-silico drug design, which have greatly accelerated development of promising candidates for new therapeutics. High-throughput screening is made more efficient through automation and machine learning, reducing the time required for identifying potential drug compounds. In addition to these advancements, AI plays a pivotal role in Absorption Distribution, Metabolism, Excretion and Toxicology (specifically predictive toxicology) evaluation, optimizing drug safety assessment. Moreover, AI-driven clinical trial design and patient recruitment are transforming the drug development process, while personalized medicine approaches are becoming more attainable. The chapter also addresses ethical considerations and regulatory challenges arising from the proliferation of AI in drug discovery and drug design. While celebrating these achievements, the chapter also outlined existing challenges and future directions for this burgeoning field which has transformative potential in revolutionizing drug discovery.