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<p>Accurate 3D structure generation from SMILES is essential for data-driven chemistry but often fails for strained ring systems. We introduce <b>strainedSMILES2xyz</b>, a Python workflow that improves conformer generation by relaxing RDKit constraints, exploring stereoisomer variants, and correcting errors using force-field refinement. Benchmarking on strained and unstrained rings shows that it outperforms existing tools, generating correct geometries in nearly all cases. The workflow is available as a Python package and Jupyter notebook.</p><p><b>Scientific Contribution</b></p><p>This work identifies a critical gap in automated SMILES-to-3D structure generation for strained molecules. Many established tools, including the widely used RDKit, frequently fail or produce incorrect geometries for these systems. By explicitly targeting these failure modes, the proposed approach enables reliable 3D structure generation for chemically relevant strained molecules within fully automated workflows.</p>

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strainedSMILES2xyz: a workflow for reliable 3D structures of strained molecules from SMILES

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Tori Demuth,
  • Julian Schnizer,
  • Dennis Svatunek

摘要

Accurate 3D structure generation from SMILES is essential for data-driven chemistry but often fails for strained ring systems. We introduce strainedSMILES2xyz, a Python workflow that improves conformer generation by relaxing RDKit constraints, exploring stereoisomer variants, and correcting errors using force-field refinement. Benchmarking on strained and unstrained rings shows that it outperforms existing tools, generating correct geometries in nearly all cases. The workflow is available as a Python package and Jupyter notebook.

Scientific Contribution

This work identifies a critical gap in automated SMILES-to-3D structure generation for strained molecules. Many established tools, including the widely used RDKit, frequently fail or produce incorrect geometries for these systems. By explicitly targeting these failure modes, the proposed approach enables reliable 3D structure generation for chemically relevant strained molecules within fully automated workflows.