Abstract
Artificial intelligence (AI) has emerged as a powerful tool that leverages human-like information processing to facilitate solutions for complex challenges. Significant advancements in AI and machine intelligence represent a transformative breakthrough in drug discovery, development, and testing of pharmaceuticals and other consumable forms. By employing AI algorithms capable of analyzing vast biological data, including genomics and proteomics, researchers can identify disease-related targets and understand their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, increasing the likelihood of successful drug approvals. Moreover, AI has the potential to reduce development costs by optimizing research and development processes. Machine learning algorithms play a crucial role in predictive design, enabling the anticipation of the pharmacokinetics and toxicity of drug candidates. This capability allows for the prioritization and selection of lead compounds, reducing the need for extensive and potentially harmful animal testing. Personalized treatment approaches can also benefit from AI algorithms that analyze patient-specific data, leading to more effective treatment outcomes and improved patient adherence. This review aims to explore and compare various applications of AI that facilitate automation and enhance productivity in drug development. Specifically, it focuses on novel drug target identification and design, drug repurposing, biomarker discovery, and patient stratification across different disease contexts. Additionally, it will highlight how these technologies are being implemented in clinical settings. This paradigm shift promises to drive further advancements in the integration of AI in automating processes within drug development, ultimately enabling more precise and personalized therapies
Published Version
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