The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics
Andrew FreiburgeriD, José P. FariaiD, Ruijie ZhuiD, Janaka N. EdirisingheiD, Filipe LiuiD, Cooper TayloriD, Vibhav SetluriD, Robert T. GiessmanniD, Moritz E. BeberiD, Elad NooriD, Vikas UpadhyayiD, Mohit Anand, Costas D. MaranasiD, Sebastian HußiD, Zoran NikoloskiiD, Adam P. ArkiniD, Robert W. CottinghamiD, Elisha M. Wood‐CharlsoniD, Arvind RamanathaniD, Christopher S. HenryiD, Samuel M. D. SeaveriD
Abstract
The ModelSEED Biochemistry Database (https://modelseed.org/biochem) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.
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