Shanghai Model Organisms Center(SH:688265) Xin Lang Cai Jing·2025-06-17 23:28Core Viewpoint - The traditional drug development model faces multiple challenges such as low efficiency, long cycles, and high failure rates, while AI is deeply reshaping the paradigm of biopharmaceutical research. However, the lack of structured, high-quality, and reusable research data resources severely restricts the value of AI algorithms in new drug development [1] Group 1: Challenges in Biomedicine - Current biomedical research in China faces challenges including a late start in data-intensive research, a lack of quality data resources, and high barriers to algorithm innovation and tool integration, which do not meet the needs for rapid modeling, precise prediction, and target identification under AI [2] - There is a disconnection between positive genetic data from humans and reverse genetic research from model organisms, preventing effective utilization of research resources [2] Group 2: AI and Phenotypic Data - Human phenotypic data and model organism phenotypic data are key nodes connecting "gene-phenotype-disease" and provide a real biological basis for AI algorithms to achieve mechanism modeling and target prediction [2] - The establishment of high-quality platforms for phenotypic data and model organisms will play an increasingly important role in the future, producing data for model training as demand increases [4] Group 3: Institutional Initiatives - Fudan University is building a resource library for experimental mice, integrating all animal facilities and planning to establish an online database for easier access [3] - The Bio-OS intelligent biological analysis tool developed by the Guangzhou National Laboratory team aims to address issues faced by researchers in data analysis, such as high development thresholds and low reusability [4] Group 4: Collaboration and Standardization - South Model Biology is collaborating with the Shanghai International Human Phenotype Group to explore standardization in phenotypic data analysis and ensure the provision of high-quality phenotypic data [4] - A proposal was made to establish "Shanghai Gene Engineering Mouse Experiment Standards" to unify model genetic backgrounds and phenotypic data collection standards [4] Group 5: Expert Participation - The seminar gathered authoritative experts and industry representatives from the fields of gene editing, phenomics, and AI computing, including professors and researchers from Fudan University and South Model Biology [5]