According to a research report, China's AI drug development market has entered a phase of rapid expansion. In 2024, the market size reached 560 million yuan, marking a year-on-year increase of 36.59%, and is projected to surpass 600 million yuan in 2025. Multiple favorable factors are converging to propel the AI drug development sector into a fast-growth trajectory. The industry is currently at a critical juncture, shifting from concept validation to commercial realization. It is advisable to focus on high-quality entities that possess advantages in platform technology, demonstrate active progress in clinical pipelines, and have clear business models. The main viewpoints are as follows:
The deep integration of artificial intelligence technology with the biopharmaceutical industry is reshaping the innovation landscape of the global pharmaceutical sector. The global AI drug development market continues to show steady growth. According to the latest data, the market size increased from $790 million in 2021 to $1.82 billion in 2024, reached $2.41 billion in 2025, and is expected to hit $2.99 billion by 2026. China's AI drug development market is developing rapidly in tandem. Starting from a size of 80 million yuan in 2020, which represented a 14.29% growth from 2019, the market's growth rate accelerated significantly to 100% in 2021, indicating the beginning of its rapid development phase. The market size further grew to 290 million yuan in 2022, jumped to 410 million yuan in 2023, reached 560 million yuan in 2024 with a 36.59% year-on-year increase, and is projected to exceed 600 million yuan in 2025.
Multiple dimensions of positive factors are resonating to push AI drug development into a rapid growth channel. On the policy front, both China and the United States have introduced supportive policies to foster industry development. The US launched initiatives to advance AI-driven scientific research, while China, through its national plans and digital transformation implementation plans for the pharmaceutical industry, has designated the application of AI in drug R&D as a core development direction. Multiple departments are jointly promoting the standardization and scaling of AI drug development, with local governments also introducing supporting measures and building public service platforms to facilitate technology application. Technologically, iterative upgrades of innovative products like AlphaFold2 and ChatGPT have fundamentally broken traditional drug R&D bottlenecks. AlphaFold2 solved the long-standing challenge of protein folding prediction, predicting over 200 million protein structures within two years. ChatGPT's generative capabilities extend to molecular design, and coupled with the emergence of new models, are driving drug R&D from "reading molecules" to "writing molecules," significantly shortening R&D cycles, improving success rates, and propelling the industry into a high-speed development period. On the demand side, the accelerating global aging population and the expanding patient base for chronic and age-related diseases are creating an urgent need for new drugs. Concurrently, pharmaceutical companies are constrained by the traditional "double ten" rule of R&D, facing continuously increasing R&D costs, and are in dire need of AI technology to empower core processes like target screening and compound synthesis to reduce costs and improve efficiency, providing sustained and strong momentum for industry growth.
The AI drug development industry chain has formed a complete division-of-labor system: "upstream data/computing support – midstream AI platform empowerment – downstream pharmaceutical company application." The upstream is centered on data resources and computing infrastructure, dominated by leading tech firms and cloud service providers. The midstream focuses on drug discovery phases like target screening and compound design, using AI to replace traditional manual processes and shorten R&D cycles, forming diversified service models from single phases to full-process services. Downstream, traditional pharmaceutical and CXO companies leverage AI technology to accelerate new drug R&D and enhance service efficiency, with end-demand in turn driving technological iteration in the midstream and upstream. Regarding the competitive landscape, in China, listed companies integrate AI technology leveraging their CXO systems or innovative drug R&D strengths. Globally, leading companies dominate through their technological accumulation, presenting a diversified and collaborative良性 competitive landscape.
Risk warnings include industry progress falling short of expectations, macroeconomic risks, and technological development not meeting forecasts.