ChemMLLM is a unified chemical multimodal large language model designed for molecule understanding and generation across text, SMILES strings, and images. The authors curated five multimodal tasks and benchmarked ChemMLLM against leading general MLLMs, chemical LLMs, and specialized models, finding it outperforms general-purpose MLLMs and matches specialized models on all tasks. The study demonstrates that a single foundation model can handle diverse cross‑modal chemical tasks, including image generation, enabling more intuitive visual human‑AI interaction.
By Qian Tan, Di Zhang, Ben Gao, Peng Xia, Wanhao Liu, Shufei Zhang, Wanli Ouyang, Lei Bai, Yuqiang Li, Tianfan Fu
ReactBench is a benchmark designed to evaluate the structural reasoning abilities of multimodal large language models (MLLMs) using chemical reaction diagrams. The dataset contains 1,618 expert‑annotated question‑answer pairs that test reasoning across four hierarchical task dimensions, from simple endpoint counting to complex topological analysis. Evaluation of 24 MLLMs shows a performance gap of more than 30% between anchor‑based tasks and holistic structural reasoning tasks, indicating that current models struggle with reasoning over branching, converging, and cyclic structures.
By Qiang Xu, Shengyuan Bai, Yu Wang, He Cao, Leqing Chen, Yuanyuan Liu, Bin Feng, Zijing Liu, Yu Li
oMeBench is a large-scale, expert-curated benchmark designed to evaluate large language models (LLMs) on organic mechanism reasoning. It contains over 10,000 annotated mechanistic steps, including reaction type labels, intermediate structures, and difficulty ratings, and introduces the oMeS scoring framework to assess logical consistency and chemical structural similarity. Evaluation shows that while current LLMs display promising chemical intuition, they often fail to produce correct and consistent multi-step reasoning, though prompting and fine-tuning can bring smaller models up to the level of closed‑source frontier models.
By Ruiling Xu, Yifan Zhang
arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.
By Arun Raja, Garrett M. Morris, Kian Ming A. Chai
arXiv:2606. 03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers.
By Hongyu Guo, Hao Li, He Cao, Gongbo Zhang, Li Yuan
arXiv:2607. 01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery.
By Wenda Wang, Yihan Tong, Yuwei Hu, Zhewei Wei
arXiv:2606. 03435v1 Announce Type: new Abstract: Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readouts to support diverse downstream tasks such as mechanism-of-action (MoA) inference, toxicity prediction, and construction of drug-disease atlases.
By Yuxin Zhang, Yiyao Li, Ping Shu Ho, Simon See, Zhenqin Wu, Kevin Tsia
Local chemical perception and property reasoning are both essential for understanding how molecular structure determines properties. Current LLM-based chemical reasoning methods either receive SMILES/molecular images together with descriptions of local motifs, or reason directly from molecular images.
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You
MolEmb is a lightweight framework that adapts multimodal large language models (MLLMs) to serve as general molecular embedding models. By aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, MolEmb produces embeddings conditioned on both a molecular profile and a natural‑language semantic context. The model performs competitively on molecular property prediction and enables cross‑modal molecule‑text retrieval, while the newly introduced MolCAR benchmark demonstrates that context‑aware molecular embedding is largely a data property of the supervision.
By Xinjian Zhao, Xiangru Jian, Yaoyao Xu, Xiaozhuang Song, Wei Pang, Lei Bai, Tianshu Yu
arXiv:2609.14779v1 Announce Type: new
Abstract: Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophi...
By Mingze Yin, Xiaohan Wang, Dian Li, Haichao Yao, Yilin Zhao, Youjun Chen, Gang Liu, Jintai Chen, Yiheng Zhu, Chang-Yu Hsieh, Aimin Pan
The paper introduces a multitask large reasoning model for molecular science that incorporates chemical knowledge via a multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. It coordinates prediction and inference specialists across ten molecular tasks—including description, generation, nomenclature translation, property prediction, and reaction prediction—using task-conditioned routing. The model surpasses more than 20 general-purpose and molecular large language models, improving aggregate performance by 50.3% and outperforming leading multitask baselines on most tasks, while maintaining interpretable chemical inference and demonstrating a workflow for CNS candidate generation and retrosynthetic planning.
By Pengfei Liu, Shuang Ge, Xiaobo Wang, Xin Liu, Jun Tao, Yan Li, Chao Liu, Ling Chen, Zhixiang Ren