🧬 Pharos Target Druggability Scraper

🚀 Pull drug-target druggability data in one run. Query the Pharos IDG knowledge base by gene symbol, or browse across its 20,000+ human protein targets.

🕒 Last updated: 2026-06-08 · 📊 Up to 17 fields per record · 20,412 targets in source · no key required

Turn the Pharos / IDG knowledge base into clean, structured target records you can drop into a target triage spreadsheet, a druggability dashboard, or a drug discovery pipeline. Look up specific genes like ACE2, EGFR, or TP53, or leave the gene list empty to browse the catalog. Each record carries the core druggability signals: target development level, protein family, novelty score, and ligand and disease association counts.

Coverage is the human protein target set that Pharos publishes from the Target Central Resource Database (TCRD), the data backbone of the NIH Common Fund's Illuminating the Druggable Genome (IDG) program. The source holds 20,412 targets at the time of writing, each classified by its Target Development Level (Tclin, Tchem, Tbio, or Tdark).

🎯 Target Audience	💡 Primary Use Cases
Drug discovery and target biology teams	Triage and rank candidate targets
Computational chemists and bioinformaticians	Seed a druggability database
Pharma and biotech analysts	Compare target development levels across a gene set
Academic researchers	Pull novelty and association data for a study

📋 What the Pharos Target Druggability Scraper does

This Actor calls the public Pharos GraphQL API and returns one clean record per target:

• By gene symbol — provide a list of symbols (for example ACE2, EGFR, TP53) and get one record per matched target, including the full publication count.
• Browse mode — leave the gene list empty and the Actor walks the catalog from the top, returning targets in the order Pharos ranks them.

Optionally enrich each record with up to 25 associated ligand names (includeLigands) and up to 25 associated disease names (includeDiseases). You control how many records come back, and every record carries a scrapedAt timestamp.

🎬 Full Demo (🚧 Coming soon)

⚙️ Input

Field	Type	Description
targets	array of strings	Gene symbols to look up, one record per match (for example ACE2, EGFR, TP53). Leave empty to browse the top targets instead.
includeLigands	boolean	Add a list of associated ligand and drug names to each record. Defaults to false.
includeDiseases	boolean	Add a list of associated disease names to each record. Defaults to false.
maxItems	integer	How many records to return. Free plan is capped at 10.

Example 1 — look up specific genes with enrichment

{
    "targets": ["ACE2", "EGFR", "TP53"],
    "includeLigands": true,
    "includeDiseases": true,
    "maxItems": 3
}

Example 2 — browse the top targets

{
    "targets": [],
    "maxItems": 50
}

⚠️ Good to Know: publicationCount is returned when you look up targets by gene symbol. In browse mode the Pharos API does not resolve it, so it comes back as null there. Use gene symbol lookups when you need the publication count.

📊 Output

Each target record looks like this:

Field	Description
🧬 name	Protein name
🏷 sym	Gene symbol
🏷 preferredSymbol	Pharos preferred gene symbol
🆔 uniprot	UniProt accession
🎯 tdl	Target Development Level (Tclin, Tchem, Tbio, Tdark)
👪 fam	Protein family (Enzyme, Kinase, TF, etc.)
✨ novelty	Pharos novelty score (lower means more studied)
🔢 tcrdid	TCRD internal target ID
🔗 url	Pharos target page URL
📝 description	Protein summary
💊 ligandCount	Number of associated ligands
💉 drugCount	Number of associated approved drugs
🦠 diseaseCount	Number of associated diseases
📚 publicationCount	Linked publications (gene symbol lookups only)
🧾 generifCount	Number of GeneRIF annotations
💊 ligands	Array of ligand names (when includeLigands is on)
🦠 diseases	Array of disease names (when includeDiseases is on)
🕒 scrapedAt	Collection timestamp
❌ error	Null on success

Real sample — ACE2

{
    "name": "Angiotensin-converting enzyme 2",
    "sym": "ACE2",
    "preferredSymbol": "ACE2",
    "uniprot": "Q9BYF1",
    "tdl": "Tchem",
    "fam": "Enzyme",
    "novelty": 0.00028408,
    "tcrdid": 15313,
    "url": "https://pharos.ncats.io/targets/Q9BYF1",
    "ligandCount": 51,
    "drugCount": 0,
    "diseaseCount": 42,
    "publicationCount": 1037,
    "generifCount": 914,
    "scrapedAt": "2026-06-08T18:04:21.000Z",
    "error": null
}

Real sample — EGFR

{
    "name": "Epidermal growth factor receptor",
    "sym": "EGFR",
    "preferredSymbol": "EGFR",
    "uniprot": "P00533",
    "tdl": "Tclin",
    "fam": "Kinase",
    "novelty": 0.00007274,
    "tcrdid": 1639,
    "url": "https://pharos.ncats.io/targets/P00533",
    "ligandCount": 2419,
    "drugCount": 26,
    "diseaseCount": 193,
    "publicationCount": 6235,
    "generifCount": 5676,
    "scrapedAt": "2026-06-08T18:04:25.000Z",
    "error": null
}

Real sample — TP53

{
    "name": "Cellular tumor antigen p53",
    "sym": "TP53",
    "preferredSymbol": "TP53",
    "uniprot": "P04637",
    "tdl": "Tchem",
    "fam": "TF",
    "novelty": 0.00001907,
    "tcrdid": 18320,
    "url": "https://pharos.ncats.io/targets/P04637",
    "ligandCount": 14,
    "drugCount": 0,
    "diseaseCount": 305,
    "publicationCount": 10522,
    "generifCount": 9473,
    "scrapedAt": "2026-06-08T18:04:29.000Z",
    "error": null
}

✨ Why choose this Actor

• One clean record per target, with the druggability signals that matter for triage up front.
• Two modes in one Actor: precise gene lookups or a catalog browse.
• Optional ligand and disease name enrichment, expanded into plain arrays.
• No account, no key, and no login required.
• Stable field names that map cleanly onto a database schema.

📈 How it compares to alternatives

Approach	Effort	Structured fields	Enrichment	Maintenance
This Actor	One run	Yes	Ligands and diseases	None on your side
Clicking through the Pharos UI	Hours	Manual copy	Manual	Constant
Writing your own GraphQL client	Days	Depends	You build it	You own the upkeep

🚀 How to use

1. Create a free Apify account using this sign-up link.
2. Open the Pharos Target Druggability Scraper.
3. Add gene symbols to targets, or leave it empty to browse the catalog.
4. Toggle includeLigands and includeDiseases if you want the enrichment, and set maxItems.
5. Click Start and grab your results when the run finishes.

💼 Business use cases

Drug discovery and target triage

Goal	How this helps
Rank a gene panel by druggability	Compare tdl, ligandCount, and drugCount side by side
Spot understudied targets	Sort by novelty and tdl Tdark

Computational biology

Goal	How this helps
Seed a druggability database	Pull structured target records in bulk
Map targets to diseases	Use diseaseCount and the diseases array

Competitive and portfolio analysis

Goal	How this helps
Track which targets have approved drugs	Read drugCount per target
Profile a target class	Group by fam (Kinase, Enzyme, GPCR, TF)

Research and reporting

Goal	How this helps
Cite literature volume per target	Use publicationCount and generifCount
Build a target one-pager	Combine description, url, and association counts

🔌 Automating Pharos Target Druggability Scraper

Connect runs to the tools you already use:

• Make and Zapier to trigger runs and route records into sheets or databases.
• Slack to post a summary when a run finishes.
• Airbyte to load results into a warehouse.
• GitHub Actions to schedule periodic snapshots.
• Google Drive to archive each run's output.

🌟 Beyond business use cases

• Research: assemble a druggability table for a paper or grant.
• Personal: explore the proteins behind a disease you care about.
• Non-profit: support open target discovery efforts.
• Experimentation: prototype a target-scoring app without writing a scraper.

🤖 Ask an AI assistant

Paste your results into ChatGPT, Claude, Perplexity, or Microsoft Copilot and ask it to rank targets by druggability, group them by family, or summarize the disease associations.

❓ Frequently Asked Questions

Do I need a Pharos or NIH account? No. The Actor reads the public Pharos GraphQL API, which needs no login.

Do I need an API key? No key is required.

What is a Target Development Level (Tdl)? Pharos classifies each target as Tclin (has approved drugs), Tchem (has potent ligands), Tbio (studied biology), or Tdark (little known). It is returned in the tdl field.

What does the novelty score mean? It is a Pharos metric where a lower value generally means the target is more studied. The Actor passes through the value the source provides.

How many targets are in Pharos? At the time of writing, the API reports 20,412 targets.

Can I look up several genes at once? Yes. Put each gene symbol in the targets list and you get one record per match.

What happens if a gene symbol is not found? The Actor writes an error record for that symbol and continues with the rest.

Why is publicationCount sometimes null? The Pharos API only resolves it in single-target gene lookups, not in browse mode. Look up by gene symbol to get it.

Can I get ligands and diseases? Yes. Turn on includeLigands and includeDiseases to add up to 25 names each.

How fresh is the data? Each run pulls live from Pharos, so it reflects the knowledge base at run time.

Can I schedule this? Yes. Use Apify Schedules to snapshot the catalog on any cadence.

🔌 Integrate with any app

Results are available through the Apify API, so you can pull them into any app, database, or workflow you already run.

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🆘 Need Help? Open our contact form

⚠️ Disclaimer: independent tool, not affiliated with Pharos, NCATS, or the NIH. Only publicly available data is collected.