Artificial Intelligence In Pharmaceutical World
DOI:
https://doi.org/10.29407/q8ykfb71Abstract
The traditional drug discovery and development process is notoriously expensive, time-consuming, and prone to high failure rates, often taking over a decade to bring a single molecule to market. This study aims to investigate the transformative role of Artificial Intelligence (AI) in modernizing the pharmaceutical industry. Utilizing a comprehensive systematic literature review framework, this research analyzes recent peer-reviewed articles, market data, and case studies detailing AI integration across the pharmaceutical value chain. The findings reveal that machine learning algorithms and generative AI foundation models significantly optimize early-stage drug design, reducing initial lead identification times from months to weeks. Furthermore, AI-driven predictive analytics enhance clinical trial designs by 30% through smarter patient selection, while automated scheduling minimizes production downtime in manufacturing. However, data silos and regulatory compliance under shifting frameworks remain critical operational bottlenecks. This study concludes that strategic AI adoption is no longer optional but a competitive imperative that reshapes pharmaceutical R&D efficiency and reduces overall costs
Keywords:
Artificial Intelligence, Drug Discovery, Clinical Trials, Pharmaceutical Industry, Machine LearningDownloads
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Copyright (c) 2026 Wanhasnah Waenawae (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
