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RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures

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RxnMiner is a multimodal agent that extracts structured reaction data from chemistry figures by combining expert-informed procedures with a chemistry toolbox. It reads molecular structures, resolves shared and entry-specific evidence, and reconstructs reaction records from templates. On the ChemRxn-Bench benchmark, RxnMiner improves stereochemistry-aware reaction macro‑F1 from 21.0% to 71.0%, outperforming existing systems and generating a large silver corpus of 88,202 reaction records from 10,107 figures.

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arXiv AI
Oct 26

RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature

RxnExBench is a multimodal benchmark designed to evaluate vision‑language models on the task of extracting structured chemical reaction data from dense figures in synthetic chemistry literature. The dataset, compiled from high‑impact journals, contains 3,565 expert‑annotated reactions and 2,361 molecular structures, organized under a unified schema of 15 reaction roles and 26 core fields. It also introduces a difficulty taxonomy, a modular annotation pipeline, and the Oracle‑MolRec protocol to separate molecular recognition from higher‑level reasoning, and shows that current state‑of‑the‑art models struggle as schema complexity and reasoning demands increase.

By Jiaxing Sun, Junyuan Gao, Ruikang Zhang, Hao Zheng, Lingli Ge, Xiao An, Jiang Wu, Huazheng Zeng, Jiahe Song, Chen Zhu, Haote Yang, Jingchao Wang, Lu Chen, Yuanyuan Cao, Ruijie Zhang, Shengxin Lu, Weijia Li, Lijun Wu, Conghui He
arXiv Machine Learning
Sep 22

Role-Aware Morgan Fingerprints for Reaction Yield Prediction

The paper introduces MFP, a reaction yield prediction method that uses role-aware Morgan fingerprints. It computes count-based circular fingerprints for each reaction component, aggregates them by chemical role, and combines them with transformation-sensitive difference features into a fixed-length descriptor for a feed-forward neural regressor. On the Suzuki‑Miyaura and Buchwald‑Hartwig benchmarks, MFP achieves R² scores of 0.878 and 0.969 respectively, while training an order of magnitude faster than graph or Transformer-based alternatives.

By Chinmay Mirji, Prashant Shekhar, Foram Madiyar, Hao Peng