Core Insights - Artificial intelligence (AI) is significantly transforming drug development processes, enhancing efficiency in target discovery, compound screening, and clinical trials [1][2][3][4][5][6] Group 1: AI in Drug Development - AI technology is shifting the drug discovery paradigm from hypothesis-driven to data-driven research, allowing for the identification of potential targets without preconceived notions [2] - The CFFF platform, developed by Fudan University and Alibaba Cloud, provides substantial computational power, enabling large-scale genomic analyses and the identification of new drug candidates [1][3] - AI has enabled the identification of significant genetic mutations associated with diseases like Parkinson's, with findings from over 1 million samples [2][3] Group 2: Efficiency in Clinical Trials - AI can optimize various aspects of clinical trials, including patient recruitment and data management, significantly reducing time and costs associated with traditional methods [5][6] - The use of AI in clinical trial design has shown to improve recruitment rates by over 30% and enhance data quality [5][6] - The global AI clinical trial market is projected to reach $2.6 billion by 2025 and exceed $22.36 billion by 2034, indicating a rapid growth trajectory [6] Group 3: Challenges and Data Issues - The industry faces challenges such as "data silos," which hinder the full potential of AI in pharmaceuticals, necessitating the creation of standardized data [7][8] - There is a growing need for trust mechanisms and integration of AI tools within clinical workflows to enhance collaboration between pharmaceutical companies and AI developers [8] - The demand for high-quality, standardized data is expected to increase as the industry progresses, highlighting the importance of addressing data fragmentation [7][8]

Core Viewpoint - Fudan University has made significant breakthroughs in the medical field, including the discovery of a new treatment target for Parkinson's disease and the upcoming launch of early screening and diagnostic testing for Alzheimer's disease, supported by AI computing power from the CFFF platform in collaboration with Alibaba Cloud [1][3]. Group 1: Breakthroughs in Alzheimer's and Parkinson's Disease - The research team led by Professor Yu Kintai has achieved a 15-year early prediction of Alzheimer's disease risk with over 98.7% accuracy, published in the journal Nature [3][5]. - The team has identified a new treatment target for Parkinson's disease and utilized AI to screen potential drugs, with findings published in top international journals such as Cell and Nature [3][5]. Group 2: CFFF Platform and AI Integration - The CFFF platform, launched in 2023, integrates advanced computing clusters and AI technologies, enabling researchers to process large datasets more efficiently than traditional methods [3][4]. - The platform supports over 1,000 parallel intelligent computations and facilitates the training of large models with billions of parameters, significantly enhancing research capabilities [3][4]. Group 3: Efficiency Improvements in Research - The use of AI and innovative data-driven methods has allowed the research team to analyze over 6,361 cerebrospinal fluid proteins, identifying five key proteins that improve diagnostic accuracy to 98.7% [4][5]. - AI technology has accelerated the identification of potential therapeutic targets in Parkinson's disease, completing in five years what would traditionally take decades [5].