AI evidence extraction

ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis

Jiawei Li, Minzhou Li, Sanzhong Luo · Nature Communications · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

5.18

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1038/s41467-026-70180-1

Methodology & findings

Study design

Design science methodology involving: (1) framework design combining LLM prompt engineering with cheminformatics tools; (2) multi-stage evaluation including benchmarking on ChEMU dataset (F1=0.983), evaluation on 50-article benchmark set from Organic Syntheses, and comparative analysis of 8 different LLMs; (3) large-scale application to 3,103 articles from Organic Syntheses volumes 1-100; (4) downstream application development (SynChat chatbot and autonomous trend analysis); (5) generalization testing on 29 newly published 2025 articles..

Primary method

Design science research with iterative evaluation and refinement. Multi-stage validation approach including: (1) component-level evaluation (image mining on 42 images, text mining on 50-article benchmark), (2) comparative analysis across 8 LLMs to inform design choices, (3) large-scale application and validation, (4) downstream application design (SynChat chatbot), (5) generalization testing on novel data.

Main result

ReactionSeek demonstrated over 95% accuracy for key reaction parameters when applied to the Organic Syntheses collection. The framework "achieved an F1 score of 0.983 in extracting critical reaction components, including reactants, products, solvents, temperature, time, and yield" on the ChEMU benchmark dataset with only seven annotated examples. The system successfully mined 3,961 unique reactions and 5,443 unique compounds from 3,103 articles spanning 1921-2021, and autonomous LLM analysis "identified decades-long historical trends in catalysis" with findings that "accurately reflect established paradigm shifts in organic synthesis from stoichiometric to catalytic approaches, from harsh to milder conditions, and from highly toxic reagents towards greener methodologies."

Research paradigm

Pragmatist/Design Science

Author conclusions

"ReactionSeek establishes a way for harnessing the vast, unstructured knowledge embedded in scientific literature. By synergistically combining the contextual understanding of LLMs with the rigor of domain-specific tools, our framework provides a robust methodology for accelerating data-driven discovery in chemistry and beyond." The authors also conclude that "ReactionSeek thus functions not merely as an extraction tool but as a foundational platform for next-generation, AI-driven chemical research."

Risk of bias

Selection bias in benchmark dataset (50 articles manually curated from Organic Syntheses may not represent full diversity); LLM model bias dependent on training data and parameter size; OCSR tool limitations (70.1% accuracy for structure recognition) introduce systematic errors; Reliance on pre-existing chemical knowledge in LLMs may disadvantage uncommon compounds; Text segmentation length bias affecting model performance on longer articles; Selection bias in benchmark dataset composition (50 articles from Organic Syntheses may not represent broader chemical literature diversity); Model selection bias: evaluation focused on specific LLM choices (GLM-4, GPT-3.5, GPT-4) which may not generalize to other architectures; OCSR tool performance dependency: InDraw achieved only 70.1% accuracy on molecular structure recognition, creating a bottleneck; Evaluation metrics tailored to practical needs rather than standardized metrics could inflate apparent performance; Limited generalization evidence: test on 29 newly published 2025 articles is modest sample size; Potential language bias in prompt engineering developed for English-language chemical literature; Selection bias: Benchmark dataset limited to 50 articles from Organic Syntheses, which are already curated, high-quality procedures; Dependency on underlying OCSR tool accuracy (70.1% for InDraw); LLM hallucination and generation errors, particularly for uncommon chemical entities; Model selection bias: Evaluation conducted on Organic Syntheses collection but generalizability to other literature styles not fully tested

Limitations

Current limitations include challenges in "(1) handling uncommon chemical names and structures, (2) interpreting complex contextual relationships dispersed across the paper, and (3) a reliance on the performance of the underlying OCSR tool and LLMs." Additionally, "the accuracy of structure recognition is influenced by the capabilities of the currently integrated OCSR tool," and "for complex compound relationships presented in tables, ReactionSeek still faces challenges in achieving satisfactory mining success rates." The system also struggles with "the aggregation of reactant quantities from multiple additions" and very long input texts that "may almost exceed the context window of the LLM."

Open questions raised

Future work should focus on: (1) enhancing robustness through more sophisticated prompt strategies, (2) integrating advanced cheminformatics tools, (3) expanding the framework's scope to capture mechanistic details, (4) extending ReactionSeek to related fields such as polymer science or materials science. The authors identify the need for better OCSR tools for R-group identification and complex structure recognition, and more advanced LLM architectures to handle uncommon chemical entities and complex contextual relationships.
Handling uncommon chemical names and structures not registered in existing databases
Interpreting complex contextual relationships dispersed across papers, particularly in tables
Improving underlying OCSR tool performance, particularly for R-group identification and complex structure recognition
Developing more sophisticated prompt strategies for enhanced robustness
Capturing mechanistic details beyond reaction parameters

Data: Organic Syntheses collection (volumes 1-100, 3,103 articles processed); ChEMU benchmark dataset used for validation; 50-article manual benchmark from Organic Syntheses; 29 articles from 2025 volume of Organic Syntheses for generalization testing; Organic Syntheses collection (publicly available, 102 volumes as of 2025, used for mining); ChEMU benchmark dataset (referenced for validation, publicly available competition dataset); 50-article benchmark set from Organic Syntheses (curated for detailed performance evaluation); 29 articles from Organic Syntheses 2025 volume (used for generalization testing); ChEMU dataset (benchmark); 50-article benchmark dataset from Organic Syntheses (manually curated); Organic Syntheses volume 101 (2025, 29 newly published articles tested)Code: Not mentioned in documentExtracted from: pdfAgreement 60%

Explore related topics

RAG for Research Literature Discovery Design Science