InChI, the IUPAC International Chemical Identifier

By Stephen R. Heller, Alan McNaught, Igor V. Pletnev, Stephen Stein, Dmitrii V. Tchekhovskoi. 2015

740 citations recorded by the source corpus.

This paper documents the design, layout and algorithms of the IUPAC International Chemical Identifier, InChI.

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1. Yuanqi Du
2. Anna Gaulton
3. Simon Axelrod
4. Gengmo Zhou
5. Tianfan Fu
6. Jon Chambers
7. A. P. Bento
8. Zhifeng Gao

Citations in the corpus

• PubChem Substance and Compound databases (2015, background): “PubChemRDF enhances cross-integration by providing direct links to available authorative RDF-based resources within applicable subdomains, including reference, synonym and InChIKey (17,18) to MeSH RDF (http://id.nlm.nih.gov/mesh/); protein to UniProt RDF (https://www.ebi.ac.uk/rdf/services/uniprot/)”
• GEOM: Energy-annotated molecular conformations for property prediction and molecular generation (2020, methodology): “The stereochemical formula utilizes perspective and node and edge notation to capture 3D orientation beyond a simple undirected graph, and can also be transformed into a string representation like SMILES [30] or InChi [31].”
• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, background): “We want to acknowledge that there exist other variants of 1D string descriptions, including International Chemical Identifier (InChI) [17], SMILES Arbitrary Target Specification (SMARTS) [18]; yet, SMILES and SELFIES are the two most commonly-used for molecule generation tasks.”
• The Cambridge Structural Database (2016, methodology): “The overlap between these resources and the CSD is identified by taking advantage of the International Chemical Identifier Standard (InChI) which provides a unique and canonical representation of the chemical substance studied (Heller et al., 2015).”
• Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “In most current MRL methods, one starts with representing molecules as 1D sequential strings, such as SMILES [16, 17, 18] and InChI [19, 20, 21], or 2D graphs [22, 11, 23, 12, 24].”
• Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, background): “The most popular line notations are SMILES [58] and InChI [59] notations (for detailed information, please refer to the supplementary material).”
• STOUT: SMILES to IUPAC names using neural machine translation (2020, background): “Other types of string representations of molecules, such as SMILES [6], InChI [7], SYBYL line notation [8], Wiswesser line notation [9], and SMARTS [10] are more concise forms of line representations.”
• BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities (2019, methodology): “InChIKey is an unambiguous chemical identifier [21].”
• BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology (2015, background): “Here, one specifies a single compound of interest as a chemical drawing, SMILES string or InChI string; or multiple compounds of interest by uploading an SDfile or a text file with one SMILES string per line.”