A Survey on Artificial Intelligence-Powered Drug Discovery and Development in Real-Life Environments Including Neonatal Therapeutics
DOI:
https://doi.org/10.52783/jns.v14.2156Keywords:
Neonatal Surgery, Healthcare, Artificial Intelligence, Machine-learning, Deep-learningAbstract
The drug discovery and development process is a complex, time-consuming, and expensive endeavor, often taking over a decade and costing billions of dollars. Artificial Intelligence (AI) has emerged as a transformative tool in this domain, offering the potential to accelerate drug discovery, reduce costs, and improve success rates. This paper provides a comprehensive survey of AI-powered approaches in drug discovery and development, focusing on their real-life applications, challenges, and future directions. We explore how AI is being used in target identification, molecular design, clinical trials, and post-market surveillance, with special attention to neonatal drug development, where AI-driven models can aid in formulating safe, effective, and personalized treatments for newborns. The paper discusses the ethical, regulatory, and technical challenges that must be addressed for widespread adoption, particularly in the development of neonatal-specific therapeutics where precision and safety are critical. Additionally, we highlight case studies, emerging trends, and the integration of AI into real-world pharmaceutical workflows, emphasizing its role in improving drug repurposing, optimizing dosage formulations, and accelerating approval processes. By examining AI's impact on both general and neonatal drug discovery, this survey serves as a valuable resource for researchers, clinicians, and pharmaceutical experts aiming to leverage AI for next-generation therapeutics.
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