SMILES all around: structure to SMILES conversion for transition metal complexes
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 ma...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Journal of Cheminformatics |
| Online Access: | https://doi.org/10.1186/s13321-025-01008-1 |
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| author | Maria H. Rasmussen Magnus Strandgaard Julius Seumer Laura K. Hemmingsen Angelo Frei David Balcells Jan H. Jensen |
| author_facet | Maria H. Rasmussen Magnus Strandgaard Julius Seumer Laura K. Hemmingsen Angelo Frei David Balcells Jan H. Jensen |
| author_sort | Maria H. Rasmussen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5fcb51dc35346d1bf16773fa9c6bcfc |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-e5fcb51dc35346d1bf16773fa9c6bcfc2025-08-20T03:52:19ZengBMCJournal of Cheminformatics1758-29462025-04-0117111310.1186/s13321-025-01008-1SMILES all around: structure to SMILES conversion for transition metal complexesMaria H. Rasmussen0Magnus Strandgaard1Julius Seumer2Laura K. Hemmingsen3Angelo Frei4David Balcells5Jan H. Jensen6Department of Chemistry, University of CopenhagenDepartment of Chemistry, University of CopenhagenDepartment of Chemistry, University of CopenhagenDepartment of Chemistry, University of CopenhagenDepartment of Chemistry, University of YorkDepartment of Chemistry, University of OsloDepartment of Chemistry, University of CopenhagenAbstract 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.https://doi.org/10.1186/s13321-025-01008-1 |
| spellingShingle | Maria H. Rasmussen Magnus Strandgaard Julius Seumer Laura K. Hemmingsen Angelo Frei David Balcells Jan H. Jensen SMILES all around: structure to SMILES conversion for transition metal complexes Journal of Cheminformatics |
| title | SMILES all around: structure to SMILES conversion for transition metal complexes |
| title_full | SMILES all around: structure to SMILES conversion for transition metal complexes |
| title_fullStr | SMILES all around: structure to SMILES conversion for transition metal complexes |
| title_full_unstemmed | SMILES all around: structure to SMILES conversion for transition metal complexes |
| title_short | SMILES all around: structure to SMILES conversion for transition metal complexes |
| title_sort | smiles all around structure to smiles conversion for transition metal complexes |
| url | https://doi.org/10.1186/s13321-025-01008-1 |
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