The global AI in drug discovery market is projected to grow from USD 1.81 billion in 2024 to USD 41.08 billion by 2040, expanding at a compound annual growth rate (CAGR) of 25%. This significant growth is fueled by advancements in key technologies such as virtual screening, predictive modeling, generative AI, and AI-driven target identification. These innovations are accelerating the drug discovery process, reducing development timelines, and lowering costs across the pharmaceutical industry. According to the report "AI in Drug Discovery Market, till 2040: Distribution by Drug Discovery Steps, Therapeutic Area, and Key Geographical Regions: Industry Trends and Global Forecasts," published by ResearchAndMarkets.com, the integration of artificial intelligence into various stages of drug development—from target identification and lead optimization to preclinical testing and clinical trial design—is transforming the landscape of pharmaceutical research. The market is segmented by drug discovery steps, including target identification, hit identification, lead optimization, preclinical development, and clinical development. AI is proving particularly impactful in early-stage discovery, where it enables researchers to analyze vast biological datasets and predict molecular behavior with greater accuracy and speed. Therapeutic areas are also seeing rapid adoption of AI tools, with oncology, neurology, and rare diseases leading the way. The complexity and high failure rates in these fields make AI-driven insights especially valuable, helping to identify novel drug candidates and improve the success rate of clinical trials. Geographically, North America currently dominates the market, driven by a strong ecosystem of biotech startups, established pharmaceutical companies, and significant investments in AI research. However, Europe and Asia-Pacific regions are expected to witness the fastest growth, supported by government initiatives, increased funding, and expanding partnerships between tech firms and life sciences organizations. The report highlights that AI is not only improving efficiency but also enabling the exploration of previously inaccessible drug targets, such as "undruggable" proteins. Generative AI models are being used to design entirely new molecules with desired properties, significantly shortening the time from concept to candidate. As AI continues to mature, its role in drug discovery is expected to evolve from a supportive tool to a core component of the R&D pipeline. With continued innovation and growing collaboration across industries, the AI in drug discovery market is poised for transformative growth over the next decade.