【招银研究|行业深度】AI应用之生物医药——科技变革初绽医药新格局

Overview - AI-driven drug development, known as AIDD, is gaining traction in the pharmaceutical industry, focusing on target and drug discovery, preclinical experiment design, clinical development, and repurposing existing drugs [1][8][9] - The demand for AI in drug development is increasing due to the rising complexity and costs associated with new drug development, with a compound annual growth rate (CAGR) of 49.7% in AI pharmaceutical investment from 2015 to 2022 [1][22][23] - The global AI pharmaceutical market is projected to reach $5.62 billion by 2028, with long-term forecasts suggesting a market size of $28 billion to $53 billion [1][23] Target and Drug Discovery - AI technology is primarily applied in target and drug discovery, utilizing traditional methods like knowledge graphs and deep learning, but still requires wet lab validation [2][28] - AI can significantly reduce the time and cost of early drug development phases, with examples showing reductions from years to months in target validation and lead compound identification [32][33] - The need for proprietary databases is increasing as AI models require high-quality data for effective target prediction [33][36] Clinical Development - The application of large language models (LLMs) in clinical development is still in its exploratory phase, but it holds significant potential for improving processes such as patient matching and trial design [55][58] - Companies like Sanofi and IQVIA are actively integrating AI technologies to automate clinical documentation and enhance research workflows [61][62] R&D Progress and Market Landscape - The majority of AI-driven drug candidates are in early stages, with many awaiting clinical data readouts, and the first fully AI-discovered drug is currently in clinical trials [63][64] - Domestic companies are making significant progress in AI drug development, with several candidates in clinical trials, indicating a competitive landscape [67] - AI-driven drug development is expected to improve clinical success rates, with studies showing higher success rates for AI-discovered molecules compared to historical averages [68] Investment Trends - The AI pharmaceutical investment landscape is vibrant, with significant funding growth from $840 million in 2015 to $14.18 billion in 2022, and a projected stable investment level in 2024 [23][25] - Major pharmaceutical companies are increasingly collaborating with AI biotech firms, with numerous transactions indicating a shift towards AI-driven platforms [71][72] Business Models and Market Dynamics - The primary business models in AI pharmaceuticals include AI+SaaS, AI+CRO, and AI+Biotech, with the latter showing greater market potential [75] - The integration of algorithms, computational power, and data is crucial for the success of AI applications in drug development, necessitating a combination of traditional and AI-driven methodologies [75]