Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines
Wei Chen, Zhiyin Yu, Liang Leng, Dan Sun, Hao Liu, Rui‐Ze Gong, Zhaotong Cong, Wenke Xiao, Guiyang Zhang, Yang Liu, Fanbo Meng, Guoqing Xu, Xiuping Yang, Qian Cheng, Zhaoyu Liu, Hongtao Liu, Jun Lü, Yufei Mao, Xiwen Li, Xinyu Tang, Dong Zhu, Hongguo Chen, Zhichao Xu, Jiang Xu, Mengqing Zhang, Zhigang Hu, Sanyin Zhang, Ruo‐Lan Du, Chao Sun, Jingyuan Song, Xiang Li, Hui Yao, Baosheng Liao, Yifei Liu, Daqing Zhao, Hang Su, Huachao Bin, Can Wang, Ting Zhang, Sheng‐Jie You, Zhaohua Shi, Lingping Zhu, Sheng‐Xiong Huang, Boli Zhang, Chi Song, Shilin Chen
Abstract
Natural components, evolved to help organisms adapt and defend against threats, are also vital sources for drug discovery due to their diverse and potent bioactivities. In the present work, we proposed the Gene-encoded Natural Diverse Components Repository (GNDC, https://cbcb.cdutcm.edu.cn/gndc/), a primary and most extensive database dedicated to cataloging diverse natural components. GNDC currently catalogs over 234 million natural components that are organized into four specialized sub-databases: HerbalMDB for 2.32 million secondary metabolites, HerbalPDB for 229 million small peptides, HerbalRDB for 2.38 million small RNAs, and HerbalCDB for 0.26 million carbohydrates. By leveraging customized pipelines for high-throughput multi-omics data and AI technologies, the GNDC enables large-scale discovery and annotation of natural products from nuclear and organellar genomes of species listed in eight global pharmacopoeias and multi-resource data. Compared to existing resources, GNDC achieves a 10-fold increase in component yield and introduces over 200 million previously unreported components. To support this unprecedented data volume and complexity, state-of-the-art AI tools are seamlessly integrated to decipher and annotate vast data collections, such as classification and gene expression signature generation of millions of secondary metabolites. We envision that the GNDC will drive the transformation of drug discovery from an "experience-driven" approach to a "big data-driven" paradigm.