CIB Postal Securities: AI pharmaceutical industry in the medical sector shifts from cost reduction and efficiency enhancement to molecular innovation, data production builds long-term barriers.

China Post Securities released a research report stating that the investment value of AI + pharmaceutical industry lies in the analysis of the current situation of the industry and future judgment. In terms of investment strategy, the firm believes that it is important to understand the current role of AI in pharmaceuticals, business models and growth space, key factors in industry growth, and competitive barriers. China Post Securities' main points are as follows: What is the role of AI in pharmaceuticals? Efficiency improvement and innovation Based on existing technology and future development, the firm believes that drug research and development based on the essence of experimental science will not be disrupted by the iterative upgrade of AI. The most mature application of AI in pharmaceuticals is to improve cost-effectiveness in preclinical stages: AI virtual screening greatly reduces the number of compounds needed in real experiments, thereby effectively shortening preclinical research and development cycles and significantly reducing R&D costs. In addition, AI molecular generation, free from cognitive bias, has innovative value. Current AI molecules (such as TNIK) have progressed to Phase 2 clinical trials and are expected to gradually realize their innovative value. The market size has exceeded 10 billion US dollars, and AI + CRO / AI + Biotech is still a major trend for companies to generate profits In terms of market space, the global AI-empowered drug development expenditure market size is expected to grow from $11.9 billion in 2023 to $74.6 billion in 2032, with a CAGR of 22.6%. The overall industry investment and financing enthusiasm is high, but domestic investments have shrunk. In the long run, the firm believes that AI + CRO / AI + Biotech is still a major trend for companies to generate profits through partnerships: on one hand, it facilitates rapid revenue generation, and on the other hand, cooperative projects can help companies iterate and upgrade their algorithm models, thus building long-term competitiveness. Algorithm + data deep integration, the production capacity of high-quality data is the core competitiveness Algorithm + data is the key production factor for the iterative upgrade of core industry technologies. The key factor in algorithm breakthroughs lies in the research and development talent team, while data competition lies in the production capacity of high-quality data (rather than the accumulation of traditional experimental data) to achieve a closed-loop and positive cycle between data accumulation and algorithm iteration. Large-scale high-quality data is a scarce resource in the industry, as the data requirements for inclusion in the training set are strict and data is not shared (creating "data islands"). Due to the involvement of core interests in experimental data, the industry's characteristic of "data islands" will not change in the long term, making high-quality data production capacity a long-term core competitiveness. Related targets: The industry is prone to forming the "Matthew effect". It is recommended to focus on leading players: INSILICO, XTALPI, PharmaResources, Hitgen Inc. Risk warning: risks of research and development failure, intensifying industry competition, geopolitical risks, policy change risks.