HELMify: A Hybrid Rule- and LLM-Based Generator of Peptide Monomer HELM Names

Clinical Snapshot

Key Findings

• P Value: Not reported; no formal statistical hypothesis testing performed

• Effect Size: Zone-based namer: 84% usable (54% exact match + 30% usable non-identical); LLM-based namer: 42% usable (33% exact match + 9% usable non-identical); LLM additionally produced 49% 'close matches' not directly usable

• Primary Outcome: Proportion of automatically generated HELM monomer names classified as 'usable' (exact match or structurally interpretable non-identical match) on a time-split test set of publicly disclosed alpha amino acids

• Nnt Or Sensitivity: Not applicable in standard clinical sense; analogous performance metric — zone-based namer positive predictive value for usable output: 84%; LLM-based namer: 42% usable, 91% producing at least a close match or better

• Confidence Interval: Not reported

Clinical Application

The zone-based namer is described as deterministic, fast, and independent of prior naming history, making it immediately deployable in high-throughput settings. The LLM-based namer requires access to a pretrained large language model with chemistry knowledge, which has infrastructure and cost implications. Neither tool eliminates the need for expert review of edge cases, particularly for novel chemical groups not covered by existing dictionaries. Direct clinical relevance to Australian patient care is limited; this is a cheminformatics infrastructure tool. However, Australian pharmaceutical research institutions (including CSIRO, Walter and Eliza Hall Institute, and university-based drug discovery centres) engaged in peptide therapeutics research would benefit from standardised HELM naming workflows. The TGA's increasing engagement with peptide-based biologics and the PBS listing of peptide drugs (e.g., semaglutide, liraglutide, octreotide) underscore the importance of robust chemical informatics infrastructure for regulatory submissions and pharmacovigilance. RACGP guidelines are not directly applicable. Australian researchers contributing to international peptide databases and collaborative drug discovery programs would benefit from the standardisation and cross-lab replication advantages described. Medicinal chemists, cheminformaticians, and computational scientists working in peptide drug discovery, particularly those managing noncanonical amino acid libraries, synthesis planning pipelines, electronic lab notebooks, and cross-institutional data exchange

Abstract

HELM is a hierarchical notation system for biopolymers that is an increasingly popular choice for representing peptides. In this system, each monomer name must uniquely identify a single monomer, and until now, chemists have named peptide monomers by hand using a limited set of rules. As the chemical space of monomers is practically unlimited, this naming process is too time-consuming to be done by chemists and is prone to inconsistency. To address this bottleneck, we developed a public benchmark data set for this task and two complementary automated naming approaches for HELM name generation: a zone-based namer, which relies on hand-curated dictionaries of substructures, and a generative AI namer, which relies on a pretrained Large Language Model (LLM) with chemistry world knowledge. We evaluated both approaches on a set of publicly disclosed alpha amino acids. For a time-split test set, the zone-based namer produced usable names for 84% of cases: 54% matched the human-generated name exactly, and an additional 30% did not match but were interpretable as the correct structure. The LLM-based namer produced usable names for 42% of cases: 33% of the names matched the human-generated name exactly, and an additional 9% were nonidentical but still interpretable as the correct structure. In another 49% of LLM-generated cases, the outputs were considered close matches, and while not directly usable, they can still inform the generation of correct names. The zone-based namer is deterministic, fast, and independent of any previous names, explicitly handling zone order and chirality. The LLM-based namer can generalize from human-named examples to propose names for novel chemical groups, helping fill library gaps. Together, the approaches offer complementary coverage: rules-based precision and speed paired with data-driven flexibility for novel moieties. Combining the strengths of both approaches paves the way for an automated peptide monomer naming process that can accelerate research by (i) helping the community establish standardized guidelines for the rapidly expanding noncanonical amino acid (ncAA) space, (ii) streamlining integration with synthesis planning, inventory management, and analysis pipelines, (iii) enabling more consistent annotation in ELNs, databases, and publications, and (iv) facilitating cross-lab replication and data exchange. Together, these advances can expedite method development, reduce ambiguity in collaboration, and ultimately support the discovery of novel peptides with improved drug-like properties.

References

1. 1.Sheridan, R. P., Shah, R., McGarty, K., Garrigou, M., Jayne, C., Roberts, A., Bryman, G. S., Zhang, L., & Samuel, E. L. G. (2026). HELMify: A hybrid rule- and LLM-based generator of peptide monomer HELM names. Journal of Chemical Information and Modeling. Advance online publication. https://doi.org/10.1021/acs.jcim.6c00408