arXiv Computer Vision By Haote Yang, Jiang Wu, Jingchao Wang, Xingjian Wei, Lixin Ma, Linye Li, Chen Zhu, Xiaolong Wu, Yuheng Lu, Ziran Zhu, Junyuan Gao, Lingli Ge, Yuan Xu, Huijie Ao, QianQian Wu, Dechen Lin, Huaiyu Gu, Lu Chen, Shengxin Lu, ShaSha Wang, Yuanyuan Cao, Zhejia Yu, Ruijie Zhang, Zimai Tian, Jiaxing Sun, Yinfan Wang, Jiahe Song, Chuang Wang, Yubin Wang, Rui Nie, Hao Zheng, Bowen Jiang, Hongbin Lai, Yifan He, Chengjin Liu, Tingting Zhang, Liqun Wei, Lijun Wu, Bin Wang, Yuqiang Li, Guangyu Wang, Wei Li, Bowen Zhou, Dahua Lin, Conghui He

MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition

Read the original on arXiv Computer Vision →

arXiv:2608. 03525v3 Announce Type: replace Abstract: In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagrams, and complex tables or figures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Machine Learning
Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han