MoleCode unlocks structural intelligence in large language models

• URL: http://arxiv.org/abs/2605.16480v1
• Date: Fri, 15 May 2026 17:44:27 GMT
• Title: MoleCode unlocks structural intelligence in large language models
• Abstract summary: MoleCode is an LLM-native, training-free, graph-explicit molecular language.<n>It makes molecular topology directly readable, editable and auditable within the language context.<n>It also changes how inference is allocated, replacing long reasoning traces devoted to implicit structural reconstruction.
• Score: 22.076506020790106
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• What Does a Chemical Language Model Know About Molecules? [0.3499870393443268]
  Chemical language models (cLMs) are widely assumed to learn surface-level syntactic patterns rather than learning meaningful molecular semantics.<n>Here, we apply sparse autoencoders (SAEs) to MolFormer, an encoder-only cLM, to mechanistically examine how molecular representations are built across layers.
  arXiv  Detail & Related papers  (2026-06-22T14:59:57Z)
• MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents [57.28652712579434]
  We present MolLingo, a multi-agent system that emulates the reasoning process of a chemist to automate molecular design.<n>MolLingo addresses this by coordinating a Literature Agent, a Chemist Agent, and an Orchestrator through a shared memory module, with each agent equipped with domain-specific tools.<n>As a case study in early-stage therapeutic design, MolLingo grounds the Chemist Agent's reasoning in binding site geometry and residue-level protein context.
  arXiv  Detail & Related papers  (2026-05-27T02:11:23Z)
• How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning? [51.286853421822705]
  Large language models (LLMs) have shown promise for reasoning-intensive scientific tasks, but their capability for chemical interpretation is still unclear.<n>We introduce a Chain-of-Thought (CoT) prompting framework and benchmark that evaluate how LLMs reason about mass spectral data to predict molecular structures.<n>Our evaluation across metrics of SMILES validity, formula consistency, and structural similarity reveals that while LLMs can produce syntactically valid and partially plausible structures, they fail to achieve chemical accuracy or link reasoning to correct molecular predictions.
  arXiv  Detail & Related papers  (2026-01-09T20:08:42Z)
• Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment [15.230372857449282]
  MolBridge is a novel molecule-text learning framework based on substructure-aware alignments.<n>We show that MolBridge effectively captures fine-grained correspondences and outperforms state-of-the-art baselines on a wide range of molecular benchmarks.
  arXiv  Detail & Related papers  (2025-10-30T05:36:31Z)
• KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge [73.51130155601824]
  We introduce KnowMol-100K, a large-scale dataset with 100K fine-grained molecular annotations across multiple levels.<n>We also propose chemically-informative molecular representation, effectively addressing limitations in existing molecular representation strategies.<n>KnowMol achieves superior performance across molecular understanding and generation tasks.
  arXiv  Detail & Related papers  (2025-10-22T11:23:58Z)
• mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules [61.161776104062774]
  We propose a modular Chemical-Language Model that understands both natural language descriptions of functions and molecular blocks.<n>mCLM front-loads synthesizability considerations while improving the predicted functions of molecules in a principled manner.
  arXiv  Detail & Related papers  (2025-05-18T22:52:39Z)
• Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model [52.84455878597969]
  Mol-LLaMA is a large molecular language model that grasps the general knowledge centered on molecules.<n>To improve molecular understanding, we propose a module that integrates complementary information from different molecular encoders.
  arXiv  Detail & Related papers  (2025-02-19T05:49:10Z)
• MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model [19.458584012046646]
  We propose a novel physicochemical knowledge-guided molecular meta language framework MolMetaLM. We design a molecule-specialized meta language paradigm, formatted as multiple S,P,O> knowledge triples sharing the same S (i.e., molecule) By introducing different molecular knowledge and noises, the meta language paradigm generates tens of thousands of pretraining tasks.
  arXiv  Detail & Related papers  (2024-11-23T09:27:38Z)
• UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation [35.277927005912275]
  We introduce UniMoT, a Unified Molecule-Text LLM adopting a tokenizer-based architecture.<n>A Vector Quantization-driven tokenizer transforms molecules into sequences of molecule tokens with causal dependency.<n>UniMoT emerges as a multi-modal generalist capable of performing both molecule-to-text and text-to-molecule tasks.
  arXiv  Detail & Related papers  (2024-08-01T18:31:31Z)
• HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment [41.75926736949724]
  We propose a graph tokenizer that encodes the hierarchy of atom, motif, and molecular levels of informative tokens to improve the molecular perception of large language models (LLMs)<n>Experiments on 14 real-world benchmarks verify the effectiveness of HIGHT in reducing hallucination by 40%, and significant improvements in various molecule-language downstream tasks.
  arXiv  Detail & Related papers  (2024-06-20T06:37:35Z)
• Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective [53.300288393173204]
  Large Language Models (LLMs) have shown remarkable performance in various cross-modal tasks. In this work, we propose an In-context Few-Shot Molecule Learning paradigm for molecule-caption translation. We evaluate the effectiveness of MolReGPT on molecule-caption translation, including molecule understanding and text-based molecule generation.
  arXiv  Detail & Related papers  (2023-06-11T08:16:25Z)
• Fragment-based Sequential Translation for Molecular Optimization [23.152338167332374]
  We propose a flexible editing paradigm that generates molecules using learned molecular fragments. We use a variational autoencoder to encode molecular fragments in a coherent latent space. We then utilize as a vocabulary for editing molecules to explore the complex chemical property space.
  arXiv  Detail & Related papers  (2021-10-26T21:20:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.