资本涌入、合作爆发，AI制药迈入加速发展期｜记“医”2025

Core Insights - The pharmaceutical industry is experiencing a transformation driven by artificial intelligence (AI), which is reshaping drug discovery and development processes, making it a focal point for global technological and pharmaceutical innovation [1][4][14] - AI-driven drug development is moving from concept validation to early value realization, attracting significant attention from capital markets, policymakers, and research institutions [1][5][14] Investment and Financing Activity - The global AI pharmaceutical sector has over 350 companies, with at least 101 based in China, most of which are in the early stages of development [5] - In 2023, there were nearly 80 investment events related to AI pharmaceuticals globally, with at least 31 occurring in China, indicating a growing interest from investors [5][6] - The trend of increasing investment in AI pharmaceuticals has been consistent over the past three years, with the number of financing events rising from 19 in 2023 to 31 in 2024, matching previous years' levels [5] Technological Advancements - AI technologies, particularly large language models and generative AI, are redefining traditional drug development paradigms, which have historically been time-consuming and costly [4][7] - New AI models are capable of generating drug molecules with potential biological activity tailored to specific diseases, significantly enhancing the drug design process [4][8] Collaboration and Partnerships - The AI pharmaceutical landscape is witnessing an increase in collaborations, with over 30 partnerships established in 2023 between multinational companies and AI-related firms, valued at approximately $10 billion [10][11] - Major pharmaceutical companies are shifting from solely in-house AI tool development to a mixed strategy that includes external collaborations, reflecting a significant change in industry mindset [10] Data Challenges - The success of AI in drug development is heavily reliant on the availability of high-quality data, which remains a challenge due to proprietary data restrictions within pharmaceutical companies [12][13] - The concept of "data capitalization" is proposed as a solution to enhance data sharing among companies, which could facilitate more effective AI model development [13] Future Outlook - As AI pharmaceutical models improve and data governance frameworks become more robust, AI-driven drug development is expected to become a key indicator of national pharmaceutical innovation competitiveness [14]