A Structure-Based Deep Learning Framework for Correcting Marine Natural Products' Misannotations Attributed to Host-Microbe Symbiosis.

ABSTRACT: 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

SUBMITTER: Tian X 

PROVIDER: S-EPMC12843494 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature