The Zhitong Finance App learned that CICC released a research report saying that the popularity of AI for Science (AI4S, artificial intelligence driven scientific research) continues to heat up, and biomedicine is the core implementation scenario. It is recommended to continue to pay attention to three types of enterprises: 1) companies that rely on differentiated technology platforms to effectively improve R&D efficiency; 2) companies that can continuously produce high-quality pipelines, are expected to achieve key clinical progress, and have the potential for external licensing; 3) enterprises with experimental feedback capabilities and automated testing systems that can take on new verification requirements.
CICC's main views are as follows:
AI pharmaceuticals' ability to improve efficiency is gradually showing, and platform capabilities and drug asset value are also being verified simultaneously
AI has gradually penetrated into protein structure prediction, virtual screening, molecular design, and pilot optimization, which is expected to reduce experimental trial and error and shorten the R&D cycle; leading the pipeline into the middle and late stages of clinical trials has provided more basis for evaluating its R&D value. The bank believes that platforms with differentiated technology that can continuously improve R&D efficiency are worth paying attention to, and that key clinical advances and potential licensing opportunities are also expected to further reflect asset value.
Capital and industrial investment continue to increase, and platform competition is more dependent on data and experimental capabilities
Technology giants are expanding from computing power and bio-based models to scientific research agents. MNC is speeding up the integration of internal data, experimental systems, and ongoing research projects with AI to promote a shift in technology exploration to R&D applications. However, insufficient high-quality causal data, difficult verification of target mechanisms, and lengthy synthetic tests still limit the transformation of computational advantages into R&D results. The bank believes that the key to establishing a differentiated advantage of the platform is to continuously generate proprietary experimental data, speed up verification feedback, and support subsequent model optimization through data accumulation.
AI expands the scope of R&D exploration, and the industrial chain is expected to simultaneously benefit from increased verification demand and service capacity upgrades
Reduced design costs can advance more projects and bring experimental demand; CRO can also combine existing experimental data, screening, and structural biology techniques to apply AI to expand service depth and improve delivery efficiency. The bank is optimistic about companies that can rely on synthesis, expression, and functional verification capabilities to meet new demand, as well as companies that use differentiated platforms and data accumulation to continuously improve R&D efficiency and service depth.
Risk Alerts
New drug development risks; increased market competition; business fluctuations or lower commercial returns than expected; data usage and computing power conditions are limited.