CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

While line notation schemes for molecular structure are well developed, they are generally unable to distinguish different conformations of the same molecule. CSMILES, an extension to the ubiquitous line notation scheme, SMILES, has been developed to address this issue. CSMILES are short strings of text that encode information characterizing the conformer structure in the maximally compact form. A conformer is defined by the dihedral angles associated with a structure that has a specified connectivity between atoms. The extension is straightforward: in the simplest case values for the dihedral angles of these bonds are determined from the atomic coordinates and added within a SMILES string at the location of the bond. For example, the canonical SMILES string of pentanol-1 is OCCCCC, and the CSMILES of one of its conformers is O{299}C{180}C{178}C{70}C{56}C. Evidently, the CSMILES strings remain readable, especially for smaller molecules. More difficult cases involving branching, rings, symmetry, and other complications have also been covered by our definitions. Further, CSMILES strings are canonicalized at the conformer level beyond simple connectivity. As such, canonical CSMILES strings are invariant to atom reordering, rigid translation, and rigid rotation. A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. Possible applications for CSMILES strings are discussed and include efficient storage of 3D structure information as well as development of machine learning models for conformation-dependent properties.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-09-26
DOI
https://doi.org/10.1021/acs.jcim.5c01613
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

Arteum D. Bochevarov, James W. Furness, Kevin B. Moore
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
article

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

Arteum D. Bochevarov, James W. Furness, Kevin B. Moore
article en

Abstract

While line notation schemes for molecular structure are well developed, they are generally unable to distinguish different conformations of the same molecule. CSMILES, an extension to the ubiquitous line notation scheme, SMILES, has been developed to address this issue. CSMILES are short strings of text that encode information characterizing the conformer structure in the maximally compact form. A conformer is defined by the dihedral angles associated with a structure that has a specified connectivity between atoms. The extension is straightforward: in the simplest case values for the dihedral angles of these bonds are determined from the atomic coordinates and added within a SMILES string at the location of the bond. For example, the canonical SMILES string of pentanol-1 is OCCCCC, and the CSMILES of one of its conformers is O{299}C{180}C{178}C{70}C{56}C. Evidently, the CSMILES strings remain readable, especially for smaller molecules. More difficult cases involving branching, rings, symmetry, and other complications have also been covered by our definitions. Further, CSMILES strings are canonicalized at the conformer level beyond simple connectivity. As such, canonical CSMILES strings are invariant to atom reordering, rigid translation, and rigid rotation. A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. Possible applications for CSMILES strings are discussed and include efficient storage of 3D structure information as well as development of machine learning models for conformation-dependent properties.

Journal of Chemical Information and ModelingVol. 65(19)
Schrodinger (United States) (US)
Openalex Percentile: Top 80%
Machine Learning in Materials Science
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