MS-Net: Multi-Similarity based network annotation for untargeted metabolomics

Abstract Confident metabolite annotation remains a critical bottleneck in untargeted LC-MS metabolomics, as experimental spectral libraries cover only 5–20% of detected features. While in silico tools generate extensive candidate lists, top-ranked predictions often fail to reflect true identities, resulting in high false annotation rates. We present MS-Net (Multi-Similarity Network-based annotation), an accessible workflow integrating mass spectral similarity networks, molecular structure similarity (Tanimoto metrics), and taxonomic knowledge to prioritize annotations within vast candidate spaces. MS-Net employs a composite Link Score combining full-molecule and scaffold Tanimoto similarities with MS/MS cosine similarity and in silico confidence metrics. High-confidence annotations seed iterative propagation throughout the network. Applied to a Cannabis sativa dataset (2,595 initial features reduced to 1,297 after filtering, from 118,000 candidates), MS-Net resolved the annotation space to 1,275 confident assignments. notably, 53% of final annotations were rescued from lower in silico ranks (2–50), demonstrating the algorithm's ability to correct ranking errors. The workflow enables reproducible, offline annotation prioritization suitable for systems biology integration.