This chapter explores the transformative role of artificial intelligence (AI) in early drug discovery, addressing the challenges of slow, expensive, and failure-prone drug development. AI-driven approaches leverage multi-omics data integration, network biology, virtual screening, and de novo molecular design to accelerate target identification, validation, and lead optimization. By analyzing vast datasets, AI uncovers novel therapeutic targets, predicts compound-target interactions, and designs tailored molecules with improved efficacy and safety profiles. The chapter highlights advancements in ultra-large virtual screening, generative models, and chemical space exploration, showcasing AI's ability to navigate and optimize complex structure-property landscapes. Case studies and emerging technologies underscore AI's potential to revolutionize early-stage drug discovery, enabling faster, cost-effective identification of promising drug candidates.