scbirlab/rasmussen-2025-csd

Organometallic SMILES: 250k compounds from CSD

These data are from Jan H. Jensen's lab at the University of Copenhagen. They published it as:

Rasmussen, M.H., Strandgaard, M., Seumer, J. et al. SMILES all around: structure to SMILES conversion for transition metal complexes. J Cheminform 17, 63 (2025). doi: 10.1186/s13321-025-01008-1. PMID: 40296090; PMCID: PMC12039060.

Dataset Details

The compounds are represented as non-standard SMILES strings that can be read by RDKit. For convenience we have added molecuar weight, Crippen cLogP, and topological surface area as calculated by RDKit (using schemist).

Dataset Description

The authors of doi: 10.1186/s13321-025-01008-1 generated SMILES from organometallic compounds in the Cambridge Structural Database, which is a repository of crystal structures of molecules.

• License: cc-by-4.0

Dataset Sources

• Repository: GitHub jensengroup/xyz2mol_tm
• Paper: doi: 10.1186/s13321-025-01008-1

Direct Use

• Chemical property prediction

Source Data

Rasmussen, M.H., Strandgaard, M., Seumer, J. et al. SMILES all around: structure to SMILES conversion for transition metal complexes. J Cheminform 17, 63 (2025). doi: 10.1186/s13321-025-01008-1. PMID: 40296090; PMCID: PMC12039060.

Who are the source data producers?

Maria H Rasmussen#, Magnus Strandgaard#, Julius Seumer, Laura K Hemmingsen, Angelo Frei, David Balcells, Jan H Jensen*

#These authors contributed equally. *Corresponding author.

Annotations

We used schemist (which in turn uses RDKit) to add molecuar weight, Crippen cLogP, and topological surface area.

Citation

BibTeX:

@article{10.1186/s13321-025-01008-1, 
year = {2025}, 
title = {{SMILES all around: structure to SMILES conversion for transition metal complexes}}, 
author = {Rasmussen, Maria H. and Strandgaard, Magnus and Seumer, Julius and Hemmingsen, Laura K. and Frei, Angelo and Balcells, David and Jensen, Jan H.}, 
journal = {Journal of Cheminformatics}, 
issn = {0028-0836}, 
doi = {10.1186/s13321-025-01008-1}, 
pmid = {40296090}, 
pmcid = {PMC12039060}, 
url = {https://www.ncbi.nlm.nih.gov/pubmed/40296090}, 
abstract = {{We present a method for creating RDKit-parsable SMILES for transition metal complexes (TMCs) based on xyz-coordinates and overall charge of the complex. This can be viewed as an extension to the program xyz2mol that does the same for organic molecules. The only dependency is RDKit, which makes it widely applicable. One thing that has been lacking when it comes to generating SMILES from structure for TMCs is an existing SMILES dataset to compare with. Therefore, sanity-checking a method has required manual work. Therefore, we also generate SMILES two other ways; one where ligand charges and TMC connectivity are based on natural bond orbital (NBO) analysis from density functional theory (DFT) calculations utilizing recent work by Kneiding et al. (Digit Discov 2: 618-633, 2023). Another one fixes SMILES available through the Cambridge Structural Database (CSD), making them parsable by RDKit. We compare these three different ways of obtaining SMILES for a subset of the CSD (tmQMg) and find >70% agreement for all three pairs. We utilize these SMILES to make simple molecular fingerprint (FP) and graph-based representations of the molecules to be used in the context of machine learning. Comparing with the graphs made by Kneiding et al. where nodes and edges are featurized with DFT properties, we find that depending on the target property (polarizability, HOMO-LUMO gap or dipole moment) the SMILES based representations can perform equally well. This makes them very suitable as baseline-models. Finally we present a dataset of 227k RDKit parsable SMILES for mononuclear TMCs in the CSD.Scientific contribution We present a method that can create RDKit-parsable SMILES strings of transition metal complexes (TMCs) from Cartesian coordinates and use it to create a dataset of 227k TMC SMILES strings. The RDKit-parsability allows us to generate perform machine learning studies of TMC properties using "standard" molecular representations such as fingerprints and 2D-graph convolution. We show that these relatively simple representations can perform quite well depending on the target property.}}, 
pages = {63}, 
number = {17}, 
volume = {1}, 
keywords = {}
}

APA:

Rasmussen, M. H., Strandgaard, M., Seumer, J., Hemmingsen, L. K., Frei, A., Balcells, D., & Jensen, J. H. (2025). SMILES all around: structure to SMILES conversion for transition metal complexes. Journal of cheminformatics, 17(1), 63. https://doi.org/10.1186/s13321-025-01008-1

Dataset Card Authors

@eachanjohnson