DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

arXiv 2026 30.3 method

TLDR

DianShi-RxnDB is a large-scale organic reaction data platform with ~24M instances, built via automated extraction from patents, and offering web/MCP access for researchers and AI agents.

Reasoning

The paper presents a valuable large-scale dataset and automated extraction pipeline with manual accuracy evaluation and comparison to Pistachio. Weaknesses include limited manual validation scope and no downstream task demonstrations showing impact on AI research.

Read-first score

Read-first score 30.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 13.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=evaluation,experiment

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 42%

10.8

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 31.

Keyword Scores

AI for scientific research

research automation

autonomous research agent

automated research

AI scientist

automated scientific discovery

literature review agent

survey generation

automated experimentation

experiment design agent

paper writing agent

scientific discovery agent

Resources

Paper