Yasin El Abiead, Jeong In Seo, Vincent Charron‐Lamoureux, Michael Strobel, Wilhan Donizete Gonçalves Nunes, Haoqi Nina Zhao, Kine Eide Kvitne, Simone Zuffa, Helena Mannochio-Russo, Harsha Gouda, Cristina Bez, Abubaker Patan, Shipei Xing, Jasmine Zemlin, Ipsita Mohany, Julius Agongo, Andres Mauricio Caraballo Rodriguez, Lindsey A. Burnett, Victoria Deleray, Abzer K Pakkir Shah, Jarmo-Charles Kalinski, Daniel Petras, Nikiforos Alygizakis, Jeremy Carver, Özgür Yürekten, Thomas Payne, Eoin Fahy, Shankar Subramaniam, Juan Antonio Vizcaíno, M. C. Wang, Pieter C. Dorrestein
Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric searches across public metabolomics repositories using molecule names or chemical representations. It queries a precomputed knowledgebase of 2.19 billion spectral matches and 420 million metadata links, supports modification-tolerant and mass-shift searches, and maps chemical structures across taxonomy, biological context and environmental conditions to accelerate discovery.