MoleCode unlocks structural intelligence in large language models

2026-05-15

AI Summary

This work proposes MoleCode, a novel, training-free molecular representation explicitly designed for large language models (LLMs) that encodes molecular graphs as human-readable and editable entities. Unlike conventional approaches relying on linear notations like SMILES—which require LLMs to implicitly reconstruct graph topology from sequential strings—MoleCode employs a Subgraph–Node–Edge syntax to explicitly represent atoms, bonds, branches, and rings with typed identifiers and explicit relational structures. This is the first method to embed molecular graphs directly and explicitly into linguistic contexts, overcoming the limitations of implicit structural encoding inherent in linear representations. MoleCode enables seamless integration of complex chemical constructs such as polymers and Markush structures, along with literature-derived chemical knowledge, significantly enhancing performance in molecular reasoning, editing, and generation tasks—particularly for unfamiliar or topologically intricate molecules—by supporting efficient, localized, and property-guided structural manipulations.

Abstract

Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses atoms, bonds, branches and rings into a compact sequence in which topology is implicit, forcing LLMs to reconstruct molecular structure before performing the requested chemical operation. Here we introduce MoleCode, an LLM-native, training-free, graph-explicit molecular language in which all molecular components are represented as typed entities with persistent identifiers and explicit relations. MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an LLM to operate on structure rather than recover it from syntax. Across molecular reasoning, editing, generation and analysis tasks, this representational shift improves frontier LLMs most strongly when structural access is limiting: unfamiliar molecules, topology-sensitive operations, larger structures and repetitive polymers. It also changes how inference is allocated, replacing long reasoning traces devoted to implicit structural reconstruction with shorter, more chemically directed reasoning over explicit atoms and bonds. In molecular optimization, this enables localized, property-aligned edits that preserve structural similarity to the starting compounds. The same Subgraph--Node--Edge grammar extends beyond small molecules to polymers, Markush structures, mechanism-style transformations and interleaved scientific documents, including research articles and patent disclosures in which chemical information is distributed across text and images. These results suggest that the interface between scientific objects and LLMs should not treat structure as something to be decoded from text. When the object of reasoning is relational, the structure itself should be part of the language.

Problem

• molecular representation
• large language models
• structural reasoning
• topology
• SMILES

Innovation

• MoleCode
• graph-explicit representation
• large language models
• molecular reasoning
• structural intelligence