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<p>The Hierarchical Editing Language for Macromolecules (HELM) provides a powerful framework for representing complex biomolecules, including peptides, oligonucleotides, and hybrid constructs, but existing tools for converting HELM notations to atomistic models suffer from limitations in speed, scope, and robustness. We introduce helmkit, an open-source Python library that enables direct, high-throughput conversion of HELM strings to RDKit molecular objects. Designed for general macromolecular structures, helmkit supports peptides, nucleic acids, chemical linkers, and hybrids, while natively handling inline monomers, special characters in names, and automatic inference of missing attachment points. Its streamlined architecture, with minimal dependencies and built-in parallelization, achieves processing speeds of up to 5,000 HELM entities per second. Validation on large-scale datasets from PubChem (878,442 entries) and CycPeptMPDB (7,298 entries) demonstrates near-perfect accuracy, with helmkit successfully parsing structures that fail in other libraries. By facilitating efficient, scalable analysis of diverse macromolecules, helmkit advances computational workflows in drug discovery, virtual screening, and biomolecular engineering. </p><p><b>Scientific contribution</b></p><p>We introduce a lightweight Python library that provides direct and accurate conversion from HELM notation to atomistic RDKit representations, addressing a practical limitation of existing cheminformatics tools. Unlike prior approaches, the library supports peptides, RNA, chemical linkers, and hybrid molecules within a single framework, while remaining robust to real-world HELM variants through built-in error correction and R-group inference. Validation on public datasets and high-throughput performance demonstrate that the method is reliable and suitable for large-scale molecular data processing.</p>

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Helmkit: fast and robust conversion of HELM notation to atomistic representations for large-scale macromolecular informatics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Ramon Adàlia,
  • Gemma Sanjuan,
  • Tomàs Margalef,
  • Ismael Zamora

摘要

The Hierarchical Editing Language for Macromolecules (HELM) provides a powerful framework for representing complex biomolecules, including peptides, oligonucleotides, and hybrid constructs, but existing tools for converting HELM notations to atomistic models suffer from limitations in speed, scope, and robustness. We introduce helmkit, an open-source Python library that enables direct, high-throughput conversion of HELM strings to RDKit molecular objects. Designed for general macromolecular structures, helmkit supports peptides, nucleic acids, chemical linkers, and hybrids, while natively handling inline monomers, special characters in names, and automatic inference of missing attachment points. Its streamlined architecture, with minimal dependencies and built-in parallelization, achieves processing speeds of up to 5,000 HELM entities per second. Validation on large-scale datasets from PubChem (878,442 entries) and CycPeptMPDB (7,298 entries) demonstrates near-perfect accuracy, with helmkit successfully parsing structures that fail in other libraries. By facilitating efficient, scalable analysis of diverse macromolecules, helmkit advances computational workflows in drug discovery, virtual screening, and biomolecular engineering.

Scientific contribution

We introduce a lightweight Python library that provides direct and accurate conversion from HELM notation to atomistic RDKit representations, addressing a practical limitation of existing cheminformatics tools. Unlike prior approaches, the library supports peptides, RNA, chemical linkers, and hybrid molecules within a single framework, while remaining robust to real-world HELM variants through built-in error correction and R-group inference. Validation on public datasets and high-throughput performance demonstrate that the method is reliable and suitable for large-scale molecular data processing.