Reaction rebalancing: a novel approach to curating reaction databases

Purpose Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets...

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Veröffentlicht in:Journal of cheminformatics Jg. 16; H. 1; S. 82 - 18
Hauptverfasser: Phan, Tieu-Long, Weinbauer, Klaus, Gärtner, Thomas, Merkle, Daniel, Andersen, Jakob L., Fagerberg, Rolf, Stadler, Peter F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 19.07.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Analysis
Carbon
Carbon compounds
Chemical compounds
Chemical properties
Chemical reactions
Chemical synthesis
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computational chemistry
Computer applications
Computer Applications in Chemistry
Data curation
Documentation and Information in Chemistry
Graph theory
Improving Reproducibility and Reusability in the Journal of Cheminformatics
Machine learning
Maximum-common-subgraph
Metabolic networks
Methodology
Methods
Neural networks
Reaction databases
Rules
SynRBL
Theoretical and Computational Chemistry
Unbalanced reactions
United States
Data curation
SynRBL
Maximum-common-subgraph
Reaction databases
Unbalanced reactions
Rules
ISSN:1758-2946, 1758-2946
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Zusammenfassung:Purpose Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need. Methods The SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities. Results The rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively. Conclusion The SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning. Scientific Contribution SynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-024-00875-4