Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI

By Noel M. O'Boyle. Journal of Cheminformatics, 2012

339 citations recorded by the source corpus.

BackgroundThere are two line notations of chemical structures that have established themselves in the field: the SMILES string and the InChI string. The InChI aims to provide a unique, or canonical, identifier for chemical structures, while SMILES strings are widely used for storage and interchange of chemical structures, but no standard exists to generate a canonical SMILES string.ResultsI describe how to use the InChI canonicalisation to derive a canonical SMILES string in a straightforward way, either incorporating the InChI normalisations (Inchified SMILES) or not (Universal SMILES). This is the first description of a method to generate canonical SMILES that takes stereochemistry into account. When tested on the 1.1 m compounds in the ChEMBL database, and a 1 m compound subset of the PubChem Substance database, no canonicalisation failures were found with Inchified SMILES. Using Universal SMILES, 99.79% of the ChEMBL database was canonicalised successfully and 99.77% of the PubChem subset.ConclusionsThe InChI canonicalisation algorithm can successfully be used as the basis for a common standard for canonical SMILES. While challenges remain – such as the development of a standard aromatic model for SMILES – the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/325dd897a18473fbde52f18b7e82e01bbf864828

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Citations in the corpus

Deep reinforcement learning for de novo drug design (2017, methodology): “ compounds for LogP, 15,549 compounds for JAK2 and 47,425 for melting temperature. All molecules were stored as normalized and canonicalized SMILES strings according to procedures developed elsewhere (77). Predictive models. We trained predictive models for three different properties – melting tempe”
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules (2017, background): “[10] A single molecule has multiple possible SMILES strings, which has led to the deﬁnition of a canonical SMILES,[11] which ensures that a molecule corresponds to a single SMILES string.”
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018, neutral): “Input Line System, or SMILES, deﬁnes grammar and syntax rules that are widely adopted in the chemistry community. In order to remove ambiguity, SMILES supports a canonical ordering (canonical SMILES) [44]. Additionally, SMILES can also encode the stereochemistry by specifying special tokens for chir”