Marine organisms are expected to be an important source of inspiration for drug discovery after terrestrial plants and microorganisms. Despite the remarkable progress in the field of marine natural products (MNPs) chemistry, there are only a few open access databases dedicated to MNPs research. To meet the growing demand for mining and sharing for MNPs-related data resources, we developed CMNPD, a comprehensive marine natural products database based on manually curated data. CMNPD currently contains more than 31 000 chemical entities with various physicochemical and pharmacokinetic properties, standardized biological activity data, systematic taxonomy and geographical distribution of source organisms, and detailed literature citations. It is an integrated platform for structure dereplication (assessment of novelty) of (marine) natural products, discovery of lead compounds, data mining of structure-activity relationships and investigation of chemical ecology. Access is available through a user-friendly web interface at https://www.cmnpd.org. We are committed to providing a free data sharing platform for not only professional MNPs researchers but also the broader scientific community to facilitate drug discovery from the ocean.

Marine natural products (MNPs) are a diverse group of bioactive compounds with varied chemical structures, but their biological origins are often misannotated due to complex host–microbe symbiosis. Propagated through public databases, such errors hinder biosynthetic studies and AI-driven drug discovery. Here, we develop a structure-based workflow of origin classification and misannotation correction for marine datasets. Using CMNPD and NPAtlas compounds, we integrate a two-step cleaning strategy that detects label inconsistencies and filters structural outliers with a microbial-pretrained graph neural network. The optimized model achieves a balanced accuracy of 85.56% and identifies 3996 compounds whose predicted microbial origins contradict their Animalia labels. These putative symbiotic metabolites cluster within known high-risk taxa, and interpretability analysis reveal biologically coherent structural patterns. This framework provides a scalable quality-control approach for natural product databases and supports more accurate biosynthetic gene cluster (BGC) tracing, host selection, and AI-driven marine natural product discovery.