The development of new drugs is a complex and high-stakes process that requires a combination of scientific discovery, technological innovation, and clinical expertise. Traditionally, drug development has been hindered by inefficiencies, including long timelines, high costs, and high attrition rates. This review explores recent innovations that are transforming the drug development pipeline, from the identification and validation of molecular targets to the final stages of clinical trials. Key advancements include the integration of artificial intelligence (AI) and machine learning, which have revolutionized target identification and drug discovery, enabling faster and more accurate predictions of drug efficacy, safety, and metabolism. Furthermore, technologies such as high-throughput screening (HTS), fragment-based drug design (FBDD), and advanced chemical engineering methods have optimized the discovery and synthesis phases, improving scalability, sustainability, and cost-effectiveness. AI-driven tools like AlphaFold have also accelerated protein structure prediction and therapeutic design. The integration of AI into drug discovery and clinical trial design holds significant promise, facilitating the shift towards personalized medicine and improving the precision of treatments. Despite challenges such as data standardization and algorithm validation, the continued evolution of these technologies is expected to reduce drug development timelines, lower costs, and bring effective therapies to market more rapidly. This review highlights the potential of these innovations to reshape the future of drug development and address previously unmet medical needs. DOI :https://doi.org/10.52783/pst.1355
Abid Ali (Tue,) studied this question.