Recently, Wall Street investment bank Bernstein listed a number of notable Chinese biomedical companies. The agency believes that AI is not a core screening standard; what really matters is whether enterprises can combine computational tools with experimental verification, clinical development, and large-scale production, and ultimately improve R&D efficiency and pipeline output. It focuses on Pharmaceutical Kangde, Cinda Biotech, Hengrui Pharmaceuticals, and Hanson Pharmaceuticals, and gives Pharmaceutical Kangde a “outperforming market” rating. It values its CRO/CDMO platform, experimental verification, and manufacturing capabilities, and believes that it is expected to become an important infrastructure for AI drug development and implementation. According to the report, Cinda Biotech, Hengrui Pharmaceuticals, and Hanson Pharmaceuticals have 13, 74, and 25 active projects respectively. Bernstein believes that compared to companies that simply emphasize the AI concept, companies with large R&D data, mature clinical systems, and continuous pipeline output have a better chance of actually transforming AI into R&D efficiency improvements and commercial results.

Recently, Wall Street investment bank Bernstein listed a number of notable Chinese biomedical companies. The agency believes that AI is not a core screening standard; what really matters is whether enterprises can combine computational tools with experimental verification, clinical development, and large-scale production, and ultimately improve R&D efficiency and pipeline output. It focuses on Pharmaceutical Kangde, Cinda Biotech, Hengrui Pharmaceuticals, and Hanson Pharmaceuticals, and gives Pharmaceutical Kangde a “outperforming market” rating. It values its CRO/CDMO platform, experimental verification, and manufacturing capabilities, and believes that it is expected to become an important infrastructure for AI drug development and implementation. According to the report, Cinda Biotech, Hengrui Pharmaceuticals, and Hanson Pharmaceuticals have 13, 74, and 25 active projects respectively. Bernstein believes that compared to companies that simply emphasize the AI concept, companies with large R&D data, mature clinical systems, and continuous pipeline output have a better chance of actually transforming AI into R&D efficiency improvements and commercial results.