China Post Securities released a research report stating that the investment value in the AI + pharmaceutical industry lies in analyzing the current industry landscape and forecasting its future trajectory. From an investment perspective, the firm believes the key is to understand the current role of AI in drug development, its business models and growth potential, as well as the critical success factors and competitive barriers within the industry. The main views of China Post Securities are as follows: What is the role of AI in drug development? Efficiency and Innovation. Based on current technology and future developments, the firm posits that the fundamental nature of drug R&D as an experimental science will not be颠覆ed by the iterative upgrades of AI. The most mature application of AI in drug development currently lies in enhancing cost efficiency during the preclinical stage: AI-powered virtual screening significantly reduces the number of compounds that need to be physically tested, thereby effectively shortening the preclinical R&D cycle and substantially cutting R&D costs. Furthermore, AI-based molecular generation, by overcoming cognitive biases, holds innovative value; current progress with AI-generated molecules (such as TNIK) has advanced to Phase 2 clinical trials, showing promise for gradually realizing this innovative potential. The market size has already exceeded ten billion USD, and the trends of AI+CRO/AI+Biotech remain the primary avenues for corporate profitability and sustainability. In terms of market space, the global market size for AI-enabled drug R&D spending is projected to grow from $11.9 billion in 2023 to $74.6 billion by 2032, representing a Compound Annual Growth Rate (CAGR) of 22.6%. Overall industry investment and financing activity remains high, although there has been some contraction domestically in China. From a long-term commercialization perspective, the firm believes AI+CRO and AI+Biotech models will continue to be the major trends for companies to generate revenue and profits: on one hand, they facilitate rapid revenue generation, and on the other, collaborative projects enable companies to iteratively upgrade their own algorithmic models, thereby building long-term competitive advantages. Algorithm and data are deeply intertwined, with the capability to produce high-quality data being the core competitiveness. The combination of algorithms and data constitutes the key生产要素 for technological iteration and upgrade within the industry. Breakthroughs in algorithms primarily depend on R&D talent and teams, while competition in data revolves around the ability to produce high-quality data (as opposed to merely accumulating traditional experimental data), aiming to create a closed-loop, positive feedback cycle between data accumulation and algorithm refinement. Large-scale, high-quality data is a scarce resource in the industry due to stringent requirements for data inclusion in training sets and the prevalent lack of data sharing (creating "data silos"). Because experimental data involves core commercial interests, the industry characteristic of "data silos" is unlikely to change in the long run; consequently, the capability to produce high-quality data represents a long-term core competitive advantage. Related Targets: The industry is prone to Matthew effects; it is advisable to focus on early leaders: Insilico Medicine, XtalPi, HongBo Pharmaceutical, and HitGen. Risk warnings include R&D failure risk, intensifying industry competition, geopolitical risks, and policy change risks.