Natural Medicinal Materials (NMMs) have a long history of global clinical applications and a wealth of records and knowledge. Although NMMs are a major source for drug discovery and clinical application, the utilization and sharing of NMM knowledge face crucial challenges, including the standardized description of critical information, efficient curation and acquisition, and language barriers. To address these, we developed ShennongAlpha, an AI-driven sharing and collaboration platform for intelligent knowledge curation, acquisition, and translation. For standardized knowledge curation, the platform introduced a Systematic Nomenclature to enable accurate differentiation and identification of NMMs. More than fourteen thousand Chinese NMMs have been curated into the platform along with their knowledge. Furthermore, the platform pioneered chat-based knowledge acquisition, standardized machine translation, and collaborative knowledge updating. Together, our study represents the first major advance in leveraging AI to empower NMM knowledge sharing, which not only marks a novel application of AI for Science, but also will significantly benefit the global biomedical, pharmaceutical, physician, and patient communities.

翻译：天然药用材料（NMMs）在全球临床应用中拥有悠久历史及丰富的记录与知识体系。尽管NMMs是药物发现与临床应用的重要来源，其知识利用与共享仍面临关键挑战，包括关键信息的标准化描述、高效整理与获取，以及语言障碍。为解决上述问题，我们开发了神农阿尔法——一个由人工智能驱动的知识智能整理、获取与翻译共享协作平台。在标准化知识整理方面，该平台引入系统命名法以实现对NMMs的精准区分与识别。目前已将超过一万四千种中文NMMs及其相关知识整理入库。此外，该平台首创了基于对话的知识获取、标准化机器翻译及协作式知识更新。本研究代表了利用人工智能赋能NMMs知识共享的重大突破，不仅标志着"人工智能驱动科学"的创新应用，更将为全球生物医学、制药、医学及患者群体带来显著效益。