Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 19, 2025Open Access

ReactionSeek: LLM-Powered Literature Data Mining and Knowledge Discovery in Organic Synthesis

View Full Paper
Ask AI
Bookmark
Share

Authors

JLJiawei LiBeijing Institute of TechnologyMLMaoguo LiMinistry of Education of the People's Republic of ChinaQYQi YangChongqing Normal University

Discussion

Loading...

Member takes

Implication

Framework combines large language models and cheminformatics to automate data mining, revealing key trends in chemical reactions.

Key Points

  • Achieving over 95% precision and recall for key reaction parameters demonstrates the effectiveness of ReactionSeek.
  • The framework utilizes large language models and cheminformatics tools to automate literature data mining for organic synthesis.
  • Applications include creating an AI-ready dataset, an interactive Synthetic Chatbot, and autonomous analysis of catalysis trends.
  • These advancements address the data curation bottleneck, paving the way for improved knowledge discovery in chemical sciences.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68af4754ad7bf08b1ead3e9dhttps://doi.org/10.26434/chemrxiv-2025-t110q
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis2026 · 7 citations
  2. 2An Autonomous Large Language Model Agent for Chemical Literature Data Mining2024 · 9 citations
  3. 3SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis2024 · 2 citations
  4. 4Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms2026
  5. 5Extracting Structured Data from Organic Synthesis Procedures Using a Fine-Tuned Large Language Model2024 · 8 citations