ChemMLLM: Chemical Multimodal Large Language Model

• URL: http://arxiv.org/abs/2505.16326v2
• Date: Mon, 04 Aug 2025 06:14:13 GMT
• Title: ChemMLLM: Chemical Multimodal Large Language Model
• Authors: Qian Tan, Dongzhan Zhou, Peng Xia, Wanhao Liu, Wanli Ouyang, Lei Bai, Yuqiang Li, Tianfan Fu,
• Abstract summary: We propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation.<n>Also, we design five multimodal tasks across text, molecular SMILES strings, and image, and curate the datasets.<n> Experimental results show that ChemMLLM achieves superior performance across all evaluated tasks.
• Score: 52.95382215206681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have made impressive progress in many applications in recent years. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation. Also, we design five multimodal tasks across text, molecular SMILES strings, and image, and curate the datasets. We benchmark ChemMLLM against a range of general leading MLLMs and Chemical LLMs on these tasks. Experimental results show that ChemMLLM achieves superior performance across all evaluated tasks. For example, in molecule image optimization task, ChemMLLM outperforms the best baseline (GPT-4o) by 116.75\% (4.27 vs 1.97 property improvement). The code is publicly available at https://github.com/bbsbz/ChemMLLM.git.

Related papers

• ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models [62.37850540570268]
  Existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. ChemEval identifies 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks. Results show that while general LLMs excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge.
  arXiv  Detail & Related papers  (2024-09-21T02:50:43Z)
• SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration [2.6421122712076244]
  We show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) We demonstrate that we can train an LLM to respond to prompts such as generating molecules with properties of interest to drug development.
  arXiv  Detail & Related papers  (2024-09-03T18:59:20Z)
• ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area [50.15254966969718]
  We introduce textbfChemVLM, an open-source chemical multimodal large language model for chemical applications.<n>ChemVLM is trained on a carefully curated bilingual dataset that enhances its ability to understand both textual and visual chemical information.<n>We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
  arXiv  Detail & Related papers  (2024-08-14T01:16:40Z)
• Benchmarking Large Language Models for Molecule Prediction Tasks [7.067145619709089]
  Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. This paper explores a fundamental question: Can LLMs effectively handle molecule prediction tasks? We identify several classification and regression prediction tasks across six standard molecule datasets. We compare their performance with existing Machine Learning (ML) models, which include text-based models and those specifically designed for analysing the geometric structure of molecules.
  arXiv  Detail & Related papers  (2024-03-08T05:59:56Z)
• ChemLLM: A Chemical Large Language Model [49.308528569982805]
  Large language models (LLMs) have made impressive progress in chemistry applications. However, the community lacks an LLM specifically designed for chemistry. Here, we introduce ChemLLM, a comprehensive framework that features the first LLM dedicated to chemistry.
  arXiv  Detail & Related papers  (2024-02-10T01:11:59Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot.<n>This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)
• 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)
• What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks [41.9830989458936]
  Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged. We aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.
  arXiv  Detail & Related papers  (2023-05-27T14:17:33Z)

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.