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TeroSeek: An AI-Powered Knowledge Base and Retrieval Generation Platform for Terpenoid Research

arXiv 2505.20663 ↗First posted 27 May 2025cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

Terpenoids are a crucial class of natural products that have been studied for over 150 years, but their interdisciplinary nature (spanning chemistry, pharmacology, and biology) complicates knowledge integration. To address this, the authors developed TeroSeek, a curated knowledge base (KB) built from two decades of terpenoid literature, coupled with an AI-powered question-answering chatbot and web service. Leveraging a retrieval-augmented generation (RAG) framework, TeroSeek provides structured, high-quality information and outperforms general-purpose large language models (LLMs) in terpenoid-related queries. It serves as a domain-specific expert tool for multidisciplinary research and is publicly available at http://teroseek.qmclab.com.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 27 May 2025.

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9 of 16 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  5. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  6. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
  7. 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 267
  8. 2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 166
  9. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145

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  3. 2024Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design
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  5. 2025RAG-Enhanced Collaborative LLM Agents for Drug Discovery
  6. 2024A quantitative analysis of knowledge-learning preferences in large language models in molecular science
  7. 2026Evaluation of LLMs in retrieving food and nutritional context for RAG systems
  8. 2024ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering