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In most main group element compounds, the valence electrons of the isolated atoms combine to form chemical bonds that satisfy the octet rule. For instance, the four valence electrons of carbon overlap with electrons from four hydrogen atoms to form CH4. The one valence electron leaves sodium and adds to the seven valence electrons of chlorine to form the ionic formula unit NaCl (Figure 1a). Transition metals do not normally bond in this fashion. They primarily form coordinate covalent bonds, a...
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SMILES all around: structure to SMILES conversion for transition metal complexes.

Maria H Rasmussen1, Magnus Strandgaard1, Julius Seumer1

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|April 28, 2025
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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.

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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.