Shree Dev Bhoomi Institute Of Education Science And Technology , Village Mazhon, P. O. Poundha (Via Premnagar), Near Jaspal Rana Shooting Range), Dehradun
Background: This abstract explores the pivotal role of artificial intelligence (AI) in revolutionizing drug development processes. Analysing vast datasets, AI facilitates target identification, drug screening, and prediction of compound interactions, expediting the discovery of novel therapeutics. The integration of AI technologies marks a transformative era in drug development, offering unprecedented opportunities for innovation and improved patient outcomes. In this review, we delve into the dynamic landscape of artificial intelligence (AI) within the realm of drug development. Our exploration encompasses a thorough examination of recent literature, dissecting how AI accelerates drug discovery, refines clinical trial processes, and propels personalized medicine initiatives. Additionally, we project into the future, unravelling potential implications that AI holds for the evolving landscape of drug development. Main body of the abstract: This review delves into AI-powered approaches, such as machine learning and deep learning GNNs, CNNs, DNNs showcasing their impact on optimizing drug design, minimizing development timelines, and enhancing overall efficiency in the pharmaceutical industry. By scrutinizing challenges and limitations, we elucidate the nuanced interplay between AI and pharmaceutical innovation. Short conclusion: AI in drug development has shown great promise in revolutionizing the pharmaceutical industry. By utilizing large datasets and advanced algorithms, AI can assist in predicting outcomes and identifying potential drug candidates. This review serves as a comprehensive guide for researchers, practitioners, and stakeholders navigating the complex and promising intersection of AI and pharmaceuticals.
Shiksha, Neha Sodiyal, Shivanand Patil, A Review On Role Of Artificial Intelligence In Drug Development, Int. J. of Pharm. Sci., 2024, Vol 2, Issue 2, 520-534. https://doi.org/10.5281/zenodo.10683990