CICC has released a research report noting that AI for Science (AI4S, artificial intelligence-driven scientific research) continues to gain traction, with biomedicine being a core application scenario. The report recommends continued attention to three types of companies: 1) companies that leverage differentiated technology platforms to effectively improve R&D efficiency; 2) companies capable of continuously producing high-quality pipelines, expected to achieve key clinical progress and possessing out-licensing potential; 3) companies with experimental feedback capabilities and automated experimentation systems that can accommodate incremental validation demands.
CICC's main views are as follows: The efficiency-enhancing capabilities of AI drug discovery are gradually emerging, and both platform capabilities and drug asset value are being validated simultaneously. AI has progressively penetrated areas such as protein structure prediction, virtual screening, molecular design, and lead optimization, potentially reducing experimental trial-and-error and shortening R&D cycles; leading pipelines are entering mid-to-late clinical stages, providing more evidence for evaluating their R&D value. The bank believes that platforms with differentiated technologies that can continuously improve R&D efficiency deserve attention, and key clinical progress as well as potential licensing opportunities are expected to further reflect asset value.
Capital and industry investment continue to increase, and platform competition increasingly relies on data and experimental capabilities. Tech giants are expanding from computing power and biological foundation models to scientific research agents, while MNCs are accelerating the integration of internal data, experimental systems, and ongoing projects with AI, driving the shift from technological exploration to R&D applications. However, insufficient high-quality causal data, difficulty in validating target mechanisms, and lengthy synthesis and testing cycles still constrain the conversion of computational advantages into R&D outcomes. The bank believes that the key to building differentiated advantages for platforms lies in continuously generating proprietary experimental data, accelerating validation feedback, and using data accumulation to support subsequent model optimization.
AI expands the scope of R&D exploration, and the industry chain is expected to benefit simultaneously from growing validation demand and upgraded service capabilities. Lower design costs can enable more projects to advance and generate experimental demand; CROs can also combine existing experimental data, screening, and structural biology technologies with AI to expand service depth and improve delivery efficiency. The bank is bullish on companies that can leverage synthesis, expression, and functional validation capabilities to accommodate incremental demand, as well as companies that utilize differentiated platforms and data accumulation to continuously improve R&D efficiency and service depth.
Risk warnings: New drug R&D risks; intensifying market competition; business fluctuations or commercial returns below expectations; limited computing power conditions for data usage.
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