Investment Rating - The industry investment rating is "Strong Buy" and is maintained [2]. Core Insights - The investment value of the AI + pharmaceutical industry lies in the analysis of the current state and future judgment of the industry. Understanding the role of AI in pharmaceuticals, its business models, growth potential, key growth factors, and competitive barriers is essential [4]. - AI in pharmaceuticals primarily enhances efficiency and innovation. The most mature applications of AI in drug development focus on cost reduction and efficiency improvements in preclinical stages, significantly shortening development cycles and reducing costs [5]. - The global market for AI-enabled drug development is projected to grow from $11.9 billion in 2023 to $74.6 billion by 2032, with a CAGR of 22.6% [5]. - The industry has seen a significant increase in investment, with the AI + CRO/AI + Biotech model being a major trend for profitability. High-quality data production capabilities are identified as a core competitive advantage [5][6]. Summary by Sections AI's Role in Pharmaceuticals - AI in drug development combines technologies like NLP and deep neural networks to enhance efficiency and expand innovation space. It integrates vast biomedical data to empower the entire drug development process [9]. - AI's application is most effective in the preclinical research phase, where it can reduce costs by over 90% and significantly shorten development timelines [21]. Market Size and Commercialization Focus - The AI + pharmaceutical financing landscape has seen rapid growth since 2015, with a total of $24.6 billion raised by 2022. However, there has been a decline in financing activity due to global economic conditions [48]. - The commercial focus is on molecular entities, with the industry not yet forming a unified paradigm, leading to structural differentiation among companies [52][68]. Business Models - The industry features three main business models: SaaS, AI + CRO, and AI + Biotech. The AI + CRO model is predominant, leveraging AI technology to provide outsourced drug development services [62][63]. - SaaS models face challenges due to limited market size and high competition, making them less favorable for new entrants [67]. Key Players and Competitive Landscape - The report highlights leading companies in the AI pharmaceutical space, including Insilico Medicine, Relay Therapeutics, and Schrodinger, which are involved in various stages of drug development [53][54]. - The competitive landscape is characterized by a "Matthew Effect," where leading players dominate due to their established capabilities and resources [6].

Investment Rating - The industry investment rating is "Strong Buy" [2]. Core Insights - The investment value of the AI+pharmaceutical industry lies in analyzing the current status and future potential of AI's role in drug development, focusing on efficiency and innovation [4]. - AI enhances drug development by reducing costs and increasing efficiency, particularly in the preclinical phase, where AI virtual screening significantly lowers the number of compounds needed for real trials, thus shortening development cycles and costs [5]. - The global market for AI-enabled drug development is projected to grow from $11.9 billion in 2023 to $74.6 billion by 2032, with a CAGR of 22.6% [5]. - The industry is experiencing a structural differentiation trend in financing, with a total of $24.6 billion raised globally for AI+drug development since 2015, although there has been a decline in financing activity in 2022 due to economic downturns [48]. Summary by Sections AI's Role in Pharmaceuticals - AI in drug development combines technologies like NLP and deep learning to enhance efficiency and expand innovation space across the entire drug development process [9]. - AI's most mature applications are in preclinical research, where it can reduce costs by over 90% and significantly shorten development timelines [22]. High-Quality Data Production as Core Competitiveness - The ability to produce high-quality data is identified as a core competitive advantage in the industry, as it enables effective algorithm iteration and data accumulation [6]. - The industry faces challenges with "data silos," where high-quality data is scarce and not shared, making data production capabilities crucial for long-term competitiveness [6]. Market Size and Commercialization Focus - The AI+pharmaceutical financing landscape has seen rapid growth, with significant investments concentrated in the US and China, although the latter's share has decreased recently [48]. - The commercial focus is shifting towards molecular entities, with AI+CRO and AI+Biotech models emerging as dominant trends for revenue generation [58]. Business Models - The industry features three main business models: SaaS, AI+CRO, and AI+Biotech, with the latter two being more prevalent due to their higher revenue potential and lower risk exposure [63][67]. - SaaS models face challenges due to limited market size and high competition, suggesting that they may not be suitable for new entrants in the industry [67].

智通财经APP获悉，中邮证券发布研报称，AI+制药行业的投资价值在于行业的现状分析和未来判断。 投资思路上来看，该行认为在于理解当下AI在制药中的作用地位、商业模式和成长空间、行业成长的 关键要素和竞争壁垒。 市场规模已超百亿美金，AI+CRO/AI+Biotech仍是企业造血盈利的大趋势 市场空间来看，全球AI赋能药物研发费用市场规模由2023年的119亿美元有望增长至2032年的746亿美 元，CAGR达22.6%。行业投融资热度整体高昂，国内有所收缩。从长远的商业化来看，该行认为 AI+CRO/AI+Biotech仍是企业造血盈利的大趋势：一方面利于快速创收，另一方面利用合作项目可以完 成企业自身算法模型迭代升级，构筑自身长期竞争力。 中邮证券主要观点如下： AI在制药中的作用是什么？增效和创新 基于现有的技术和未来的发展状况，该行认为药物研发基于实验科学的本质不会因AI的迭代升级而发 生颠覆。AI在制药端最成熟的落地应用在于提升了临床前的降本增效：AI虚拟筛选极大地降低了真实 试验中所需要投入的化合物的数量，从而有效缩短了临床前研发周期、大幅减少研发成本。此外，AI 分子生成摆脱认知偏见具有创新价值， ...