SMILES all around: structure to SMILES conversion for transition metal complexes.
Maria H Rasmussen1, Magnus Strandgaard1, Julius Seumer1
1Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
Journal of Cheminformatics
|April 28, 2025
Summary
We developed a method to generate RDKit-parsable SMILES for transition metal complexes (TMCs) from their 3D structures. This enables machine learning studies using standard molecular representations, creating a large dataset of TMC SMILES.
Area of Science:
- Computational chemistry
- Materials science
- Cheminformatics
Background:
- Generating accurate SMILES strings for transition metal complexes (TMCs) is challenging due to their unique bonding.
- Existing methods often require manual validation or lack broad applicability.
- A standardized, parsable representation is needed for large-scale computational studies.
Purpose of the Study:
- To present a novel method for generating RDKit-parsable SMILES for TMCs from xyz-coordinates.
- To create a comprehensive dataset of TMC SMILES for machine learning applications.
- To evaluate the performance of SMILES-based molecular representations for predicting TMC properties.
Main Methods:
- Developed a method to convert xyz-coordinates and charge into RDKit-parsable SMILES.
- Generated SMILES using Natural Bond Orbital (NBO) analysis and by correcting Cambridge Structural Database (CSD) entries.
- Compared three SMILES generation strategies on a CSD subset (tmQMg), achieving >70% agreement.
- Created molecular fingerprints and graph representations from SMILES for machine learning.
Main Results:
- The new method produces RDKit-parsable SMILES for TMCs.
- Comparison of three methods showed over 70% agreement for SMILES generation.
- SMILES-based molecular fingerprints and graph representations performed comparably to DFT-based methods for predicting properties like polarizability and dipole moment.
- A dataset of 227,000 RDKit-parsable SMILES for mononuclear TMCs was generated.
Conclusions:
- The developed method provides a reliable way to generate parsable SMILES for TMCs.
- SMILES-based representations are effective and efficient baselines for machine learning on TMCs.
- The generated dataset facilitates further research in computational materials science and drug discovery.
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