🧬 KEGG Pathways Scraper

🚀 Export biological pathway data from KEGG in seconds. List, search, or fetch entries across 18 KEGG databases (pathway, module, KO, compound, glycan, reaction, enzyme, drug, disease, and more). No login, no API key.

The KEGG Pathways Scraper taps KEGG (Kyoto Encyclopedia of Genes and Genomes), the reference resource for understanding biological systems from genomes and pathways to drugs and diseases. The Actor returns 20 structured fields per record, including the entry ID, name, organism, definition, description, classification, cross-references to modules, diseases, drugs, pathways, genes, compounds, reactions, enzymes, and literature citations.

The catalog covers 18 KEGG databases, from pathway and module to compound, glycan, reaction, rclass, enzyme, network, variant, disease, drug, dgroup, plus the organism-specific KO orthology and viral/addendum genomes. This Actor exposes three query modes (list, find, get) so you can browse, search, or pull full detail with the same predictable schema.

🎯 Target Audience	💡 Primary Use Cases
Systems biologists, drug-discovery scientists, metabolic engineers, bioinformaticians, computational biologists, pharma research, biotech startups	Pathway enrichment analysis, drug-target identification, metabolic network modelling, disease-gene mapping, ortholog assignment, KEGG-driven LLM grounding

📋 What the KEGG Pathways Scraper does

Three query workflows across 18 databases in a single Actor:

• 📑 List mode. Enumerate every entry in a database (e.g. all human pathways, all compounds, all drugs).
• 🔎 Find mode. Free-text keyword search across a database (e.g. glycolysis, insulin, tuberculosis).
• 🧾 Get mode. Retrieve full entry detail by ID (e.g. hsa00010+hsa00020 for human glycolysis and TCA cycle).
• 🧬 Organism filter. Restrict pathway/module/KO queries to one organism via KEGG codes (hsa human, mmu mouse, eco E. coli, sce yeast).

Each record includes the entry ID, name, organism, definition, description, classification hierarchy, plus structured cross-references to modules, diseases, drugs, related pathways, genes, compounds, reactions, enzymes, and literature references. The raw KEGG flat-file text is also preserved.

💡 Why it matters: KEGG is the most widely cited pathway resource in systems biology, but its REST API returns plain-text flat files that need a custom parser per database. This Actor delivers a single normalized schema, so you can move from query to dashboard or model in minutes.

📊 Data fields

Each record includes: classification, compounds, database, definition, description, diseases, drugs, entryId, enzymes, genes, modules, name, organism, pathways, rawEntry, reactions, references, scrapedAt, url. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset.

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the KEGG Pathways Scraper page on the Apify Store.
3. 🎯 Set input. Pick a database, query mode, and any keyword or ID list.
4. 🚀 Run it. Click Start and let the Actor collect your data.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

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💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by KEGG, Kanehisa Laboratories, Kyoto University, or Pathway Solutions Inc. All trademarks mentioned are the property of their respective owners. Only publicly available open data is collected.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

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