Abstract
Artificial intelligence (AI) has become an increasingly transformative tool in pharmaceutical drug discovery, offering innovative approaches to solving complex problems that have traditionally slowed down the drug development process. AI technologies, including machine learning (ML), natural language processing (NLP), and deep learning (DL), are being leveraged to analyze large datasets, predict molecular interactions, and optimize drug design processes. These AI-driven approaches enable faster identification of potential drug candidates, more accurate predictions of biological activity, and the discovery of novel therapeutic targets. This article explores the key applications of AI in drug discovery, focusing on drug target identification, drug screening, and predictive modeling for clinical trials. The challenges and future directions for integrating AI into pharmaceutical research are also discussed, highlighting its potential to accelerate the development of safe and effective drugs.
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