Tautomer Database
Tautomer Database provides a curated collection of experimentally derived tautomeric structures to support analyses of tautomerism and studies of tautomeric equilibria.
Key Features:
- Comprehensive Data Collection: The database compiles 5,977 unique chemical structures extracted from 171 scientific publications covering 2,819 distinct tautomeric tuples (pairs, triples, and higher-order cases) and includes bibliographic references, structural identifiers (NCI/CADD FICTS, FICuS, uuuuu, Standard InChI), SMILES notation, and molecular weight.
- Types of Tautomerism: Entries are categorized by tautomerism type with prototropic tautomerism comprising 79%, ring–chain 13%, and valence 8% of cases.
- Experimental Conditions: The dataset records experimental context including 50 pure solvents, 9 solvent mixtures, and 26 spectroscopic or nonspectroscopic methods with 1H and 13C NMR as the most commonly used techniques.
- Tautomeric Transform Rules: The resource includes examples of 77 distinct tautomeric transform rules encoded as SMIRKS.
Scientific Applications:
- Chemical Equilibria Analysis: Supports quantitative and qualitative studies of tautomeric equilibria across documented solvents and methods.
- Drug Discovery: Provides experimentally observed tautomeric states to inform considerations of molecular flexibility and conformational variability in ligand design.
- Computational Chemistry: Supplies experimentally derived tautomer examples and transform rules for benchmarking and improving tautomer-prediction methods.
Methodology:
Entries contain bibliographic references and computational representations including structural identifiers (NCI/CADD FICTS, FICuS, uuuuu, Standard InChI), SMILES notation, molecular weights, and examples of 77 tautomeric transform rules encoded as SMIRKS.
Topics
Details
- Added:
- 1/18/2021
- Last Updated:
- 2/26/2021
Operations
Publications
Dhaked DK, Guasch L, Nicklaus MC. Tautomer Database: A Comprehensive Resource for Tautomerism Analyses. Journal of Chemical Information and Modeling. 2020;60(3):1090-1100. doi:10.1021/acs.jcim.9b01156. PMID:32027495. PMCID:PMC8456363.
PMID: 32027495