Artificial Intelligence in Drug Design and Clinical Trials

Abstract

Artificial intelligence (AI) has emerged as a transformative force in the pharmaceutical industry, significantly enhancing drug discovery, development, and clinical trials. AI-driven algorithms streamline target identification, facilitate molecular design, and optimize lead compounds, accelerating the traditional drug development process. By leveraging machine learning, deep learning, and neural networks, AI enables pharmaceutical companies to analyze vast datasets, predict drug interactions, and reduce the time and costs associated with bringing new drugs to market. Beyond discovery, AI is also revolutionizing clinical trials by optimizing patient recruitment, predicting trial outcomes, and automating regulatory workflows. These advancements contribute to increased efficiency, improved success rates, and more personalized treatment approaches. However, integrating AI into pharmaceutical workflows presents challenges, including data quality concerns, ethical considerations, and regulatory hurdles. This chapter explores AI’s growing impact on pharmaceutical innovation, detailing its applications, benefits, and limitations while considering future challenges and solutions. As AI continues to evolve, its potential to drive efficiency and improve patient outcomes will shape the future of drug development and healthcare.

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