Abstract
Protein kinases play a pivotal role in regulating cellular functions and signaling pathways, with over 530 kinase genes identified in the human genome. Traditional drug discovery targeting kinases is a lengthy and costly process, often exceeding 15 years and $1 billion. However, artificial intelligence (AI) offers transformative potential to streamline this pipeline by accelerating target identification, validation, and drug design. AI-driven methodologies such as machine learning, deep learning, and natural language processing have revolutionized preclinical drug discovery, enabling rapid identification of drug candidates, structural predictions, and binding affinity assessments. Tools such as AlphaFold2, OnionNet-2, and Deeply Tough illustrate AI’s role in structural and virtual screening of drugs. AI has also significantly impacted clinical trials through digital twinning, patient recruitment optimization, and real-time monitoring, enhancing trial efficiency and precision. Currently, 75 AI-powered drugs are in various clinical phases, with notable successes in novel target identification and drug repurposing. Despite its promise, AI in drug discovery faces challenges such as ethical concerns, data quality, and substantial investment requirements. Looking ahead, advances in organoid intelligence and biocomputing hold promise for a future where AI-driven innovation accelerates drug discovery, enhances precision medicine, and transforms the pharmaceutical landscape.
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