MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models

• URL: http://arxiv.org/abs/2608.23646v1
• Date: Mon, 24 Aug 2026 09:21:04 GMT
• Title: MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
• Abstract summary: Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery.<n>We ask whether multimodal large language models (MLLMs) can serve as emphgeneral molecular embedding models.<n>We introduce textbfMolEmb, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space.
• Score: 21.71979155816693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most molecular encoders are specialist models built around a single molecular view, producing unconditional vectors with no language interface for varying the representation. We ask whether multimodal large language models (MLLMs), which natively process images, text, and symbolic inputs, can instead serve as \emph{general molecular embedding models} that produce embeddings conditioned on both a molecular profile and a natural-language semantic context. We introduce \textbf{MolEmb}, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective. The resulting embedding model is competitive on molecular property prediction and supports cross-modal molecule--text retrieval in the same space. We further introduce \textbf{MolCAR}, a diagnostic benchmark for context-aware retrieval, and find that context-aware molecular embedding is primarily a data property of the supervision. These results suggest that MLLMs are not merely chemistry assistants or generators, but a viable and extensible route to general molecular embedding models.

Related papers

• MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding [25.768840369692608]
  MolSight is a graph-aware vision-language model framework designed to enhance the understanding of molecular images.<n>Our experiments demonstrate that MolSight significantly outperforms existing VLMs, molecular LLMs, and specialized tools across multiple chemical visual understanding tasks.
  arXiv  Detail & Related papers  (2026-07-02T10:13:19Z)
• 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)
• MolDA: Molecular Understanding and Generation via Large Language Diffusion Model [7.901276712572294]
  MolDA is a novel framework that replaces the conventional AR backbone with a discrete Large Language Diffusion Model.<n>It ensures global structural coherence, chemical validity, and robust reasoning across molecule generation, captioning, and property prediction.
  arXiv  Detail & Related papers  (2026-04-06T04:04:14Z)
• 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)
• $\text{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models [59.125833618091846]
  We propose a multi-view framework that integrates three perspectives: the molecular structure view, the molecular task view, and the molecular rules view.<n>Experiments demonstrate that $textM2$LLM achieves state-of-the-art performance on multiple benchmarks across classification and regression tasks.
  arXiv  Detail & Related papers  (2025-08-12T05:46:47Z)
• Chemical Language Model Linker: blending text and molecules with modular adapters [2.2667044928324747]
  We propose a lightweight adapter-based strategy named Chemical Language Model Linker (ChemLML)<n>ChemLML blends the two single domain models and obtains conditional molecular generation from text descriptions.<n>We find that the choice of molecular representation used within ChemLML, SMILES versus SELFIES, has a strong influence on conditional molecular generation performance.
  arXiv  Detail & Related papers  (2024-10-26T13:40:13Z)
• 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)
• MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension [44.97089022713424]
  Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields.<n>This study seeks to enhance the ability of LLMs to comprehend molecules by equipping them with a multi-modal external module, termed MolX.<n>A hand-crafted molecular fingerprint is incorporated to leverage its embedded domain knowledge.
  arXiv  Detail & Related papers  (2024-06-10T20:25:18Z)
• LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models [55.5427001668863]
  We present a novel latent diffusion model dubbed LDMol for text-conditioned molecule generation.<n> Experiments show that LDMol outperforms the existing autoregressive baselines on the text-to-molecule generation benchmark.<n>We show that LDMol can be applied to downstream tasks such as molecule-to-text retrieval and text-guided molecule editing.
  arXiv  Detail & Related papers  (2024-05-28T04:59:13Z)
• Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model [49.64512917330373]
  We introduce a multi-constraint molecular generation large language model, TSMMG, akin to a student. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers' We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements.
  arXiv  Detail & Related papers  (2024-03-20T02:15:55Z)
• 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)
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [63.60376252491507]
  We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
  arXiv  Detail & Related papers  (2022-09-12T00:56:57Z)

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.