At the roundtable discussion titled "From Follower to Definitive Force: Are Chinese Pharma Companies Becoming Global Innovation Leaders?" held on November 13 during the 2026 Shanghai Stock Exchange International Investor Conference, Shanghai Innostar Bio-Tech Co.,Ltd. (SSE: 688710) Chair and President Chang Yan stated that over the past six months, the most significant change in China's innovative drug industry has not merely been the continuous growth in the number of innovative drug assets, but also the steady improvement in asset quality and value.
Chang Yan noted that when the market previously observed the growth in outbound licensing deals for Chinese innovative drugs, attention focused primarily on whether the number of related assets could be sustained. However, industry developments since the start of this year demonstrate that Chinese innovative drug R&D has already established systematic technology platforms and development frameworks, and the value of innovative drug assets has been continuously enhanced.
She pointed out that a deeper manifestation of the industry shift is the ongoing transformation in the mindset of innovative drug researchers. In the past, some R&D activities placed greater emphasis on time and speed, but now, at every decision-making stage from project initiation to the advancement of R&D pipelines, companies are paying closer attention to how data can prove product value and competitive advantages, as well as what R&D goals they hope to achieve in the future.
"In the future, more and more concepts from translational medicine and translational science will be integrated into the process of advancing products from project initiation through non-clinical studies to clinical proof of concept," Chang Yan said. This shift is driving innovative drug R&D toward greater emphasis on clinical value and early-stage evidence, and will serve as an important foundation for China's innovative drug industry to continuously deliver high-quality assets.
Regarding the application of artificial intelligence, Chang Yan stated that Innostar has already been exploring AI applications in areas such as automated reporting and automated translation, while also studying how to use AI to help clients obtain higher-quality data, save R&D time and costs, including exploring directions such as AI virtual controls. She believes that a more ideal application goal for AI is not only to generate new molecules, but also to possess more systematic predictive capabilities. How to effectively integrate data from different levels — including pharmaceutical sciences, non-clinical research, as well as biology, animal and human data — to enhance the ability to predict innovative drug R&D outcomes remains a challenge the industry needs to address.
Chang Yan expects that as the closed loop between front-end AI-driven R&D and experimental validation gradually improves, the efficiency of AI-generated molecules is expected to further increase, and within the next two to three years, related applications may see more pronounced large-scale changes.
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