Core Viewpoint - The discovery and identification of therapeutic targets remain a critical bottleneck in drug development, with over 90% of candidate drugs failing in clinical development due to flawed initial hypotheses regarding biological function, disease relevance, or druggability [2][3]. Group 1: Target Discovery Challenges - Traditional target discovery relies on disease biologists integrating various independent biomedical data to form testable hypotheses, which is a slow and costly process, often exceeding $2 million per target [2][3]. - The failure rate in clinical development is largely attributed to issues with the selected targets rather than the compounds themselves [2]. Group 2: Introduction of OriGene - A new multi-agent virtual disease biologist system named "OriGene" has been developed, focusing on target discovery and clinical translation value assessment, outperforming human experts and leading AI models in target discovery capabilities [2][3][9]. - OriGene autonomously discovered new targets for liver cancer and colorectal cancer, demonstrating its ability to generate original targets validated through experiments [3][27]. Group 3: System Features and Functionality - OriGene integrates over 500 expert tools and organized biomedical databases, supporting multi-modal reasoning across genomics, transcriptomics, proteomics, phenomics, and pharmacology [11][12]. - The system features a multi-agent collaborative decision-making architecture, including a Coordinator Agent, Planning Agent, Reasoning Agent, Critic Agent, and Reporting Agent, enabling a closed-loop autonomous scientific decision-making process [12][13]. Group 4: Performance Evaluation - A specialized benchmark test set for target discovery, TRQA, was created, covering 1,921 multi-dimensional validation questions, demonstrating OriGene's superior performance in accuracy, recall, and robustness compared to human experts and other AI models [18][21]. - The system's self-evolving capabilities allow it to improve its reasoning ability over time through iterative learning and feedback from experiments [14][16]. Group 5: Practical Validation - In liver cancer, OriGene identified G protein-coupled receptor GPR160 as a key target, showing significant expression in cancer tissues and potential as a new immune checkpoint [23]. - For colorectal cancer, the system selected arginase ARG2 as a target, confirming its high expression in cancer tissues and demonstrating effective tumor suppression in patient-derived organoid models [25][27]. Group 6: Implications for Drug Development - The research signifies a major advancement in using AI to accelerate therapeutic target discovery, providing a scalable and adaptable platform for identifying mechanism-based treatment targets [27]. - As generative AI models and biomedical data resources mature, frameworks like OriGene are expected to facilitate AI-driven end-to-end drug discovery, enhancing the potential for precision medicine [27].