Barnika Debnath
1 
, Samson K Wilson
2, Subhrajyoty Basu
2, Sai Yasasvi Kompella
2, Rakesh Singha
2, Santosh Kumar Sahoo
3, Nitin Yadav
2, Partha Roy
2, Amit Kundu
2* 1 Department of Pharmaceutical Analysis, School of Pharmacy, GITAM(Deemed to be University, Visakhapatnam, India
2 Department of Pharmacology, School of Pharmacy, GITAM(Deemed to be University, Visakhapatnam, India
3 Department of Pharmaceutical Chemistry, School of Pharmacy, GITAM(Deemed to be University, Visakhapatnam, India
Abstract
In the pharmaceutical industry, artificial intelligence (AI) is revolutionizing individualized therapy, research, and drug development. AI includes machine learning (ML) and deep learning (DL), that are used to read enormous amounts of data, spot mysterious patterns, and find possible medication candidates more quickly. AI is also improving clinical trials through better patient recruitment, real-time data monitoring, and trial outcome prediction. It also customizes care according on a person’s genetic composition, lifestyle, and environmental factors is also supporting personalized medicine, a novel approach to healthcare. In, pharmaceutical industries it is used to simplify medicine production procedures, enhancing quality control, and streamlining supply chain management that saves the valuable time as well as billions of dollars. This comprehensive review discusses the different impacts of AI-enabled technologies on each stage of the pharmaceutical life cycle. It demonstrates that ML, data analytics and predictive modelling can accelerate drug discovery, improve manufacturing processes, streamline quality processes, enhance formulation approaches, and transform post-marketing surveillance, drug repurposing, precision medicine, and nanobots.