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CGsmiles: A Versatile Line Notation for Molecular Representations across Multiple Resolutions
Fabian Grünewald1,2, Leif Seute1, Riccardo Alessandri3
1Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.
Journal of Chemical Information and Modeling
|March 24, 2025
Summary
We introduce CGsmiles, a new notation for coarse-grained (CG) models, enabling efficient chemical space exploration and multiresolution molecular representation. This format addresses limitations in current methods, facilitating high-throughput screening and machine learning applications.
Area of Science:
- Computational Chemistry
- Cheminformatics
- Materials Science
Background:
- Coarse-grained (CG) models simplify molecular representations for faster simulations, crucial for high-throughput (HT) screening and exploring vast chemical spaces.
- Existing CG models lack standardized data formats for describing atom grouping (mapping), hindering scalability and data sharing.
- The Martini force field exemplifies CG models but requires robust notation for its growing molecular library.
Purpose of the Study:
- To introduce CGsmiles, a novel line notation for representing coarse-grained molecules and their hierarchical relationships.
- To address the lack of standardized mapping and indexing capabilities in current coarse-grained modeling.
- To demonstrate the utility of CGsmiles in facilitating multiresolution analysis and machine learning applications.
Main Methods:
- Developed CGsmiles, a line notation inspired by SMILES and BigSMILES, encoding molecular graphs and particle properties.
- Incorporated a framework for seamless conversion between coarse-grained and fine-grained resolutions within a single string.
- Analyzed a benchmark set of 407 molecules from the Martini force field using the CGsmiles syntax.
Main Results:
- CGsmiles successfully encodes molecular graphs and particle properties, independent of resolution.
- The notation enables representation of multiple resolutions and hierarchical relationships in a single string.
- Demonstrated CGsmiles' utility by constructing machine learning models for partition coefficient prediction using multiresolution data.
Conclusions:
- CGsmiles provides a versatile and rigorous solution for describing coarse-grained models and their mappings.
- The notation overcomes significant barriers in coarse-grained modeling, enabling true high-throughput capabilities.
- CGsmiles is applicable to polymers and other complex systems, enhancing molecular simulation and data analysis.
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