The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics
Freiburger, A.; Faria, J. P.; Edirisinghe, J. N.; Liu, F.; Taylor, C.; Setlur, V.; Giessmann, R. T.; Beber, M. E.; Noor, E.; Upadhyay, V.; Anand, M.; Maranas, C. D.; Huss, S.; Nikoloski, Z.; Arkin, A. P.; Cottingham, R. W.; Wood-Charlson, E. M.; Henry, C. S.; Seaver, S. M. D.
Show abstract
The ModelSEED Biochemistry Database (https://modelseed.org) 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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes 94%
- Signatures of cell death and proliferation in perturbation transcriptomics data - from confounding factor to effective prediction 91%
- Cat_Wiz: A stereochemistry-guided toolkit for locating, diagnosing and annotating Mg2+ ions in RNA structures. 91%
Similar papers in this journal
- Automated genome mining predicts structural diversity and taxonomic distribution of peptide metallophores across bacteria 92%
- A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome 91%
- Deep Learning Reveals Endogenous Sterols as Allosteric Modulators of the GPCR-Gα Interface 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.