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OpenScience template

AI Drug Discovery Agent (UniProt/PDB)

Target briefs for AI drug discovery, grounded in UniProt, PDB and ChEMBL accessions you can click and check. Bring one model key. Nothing docked or predicted.

NewNew10.5 KB snapshotPro VM

What's inside

Harness

OpenScience

Plan

Pro

vCPU

2

Memory

6 GiB

Snapshot

10.5 KB

Setup prompt + walkthrough

The AI Drug Discovery Agent (UniProt/PDB) setup prompt + walkthrough

Paste this into any capable agent and it walks you through the connections, the trigger, and a supervised first run — the whole setup, in your own words. Or skip the setup and run this exact agent hosted.

Set up an agent for me, in its own chat, that does the work of a AI Drug Discovery Agent (UniProt/PDB). Work from the files and facts I actually give it, rank what matters by impact rather than by how many items it found, and stop short of sending, filing, posting, or changing anything live unless I ask for that in the same run. Walk me through connecting Anthropic, Gemini, OpenAI, and OpenRouter. Have it run whenever I hand it the work, and every weekday morning on whatever I left unfinished. Ask me where the source material lives, who should receive the output, and what good enough looks like for a first pass. Run one pass while I watch, then save it.

Already built, running in ~3 min · 30-day money-back on first purchase

How it works · ~3 minutes

  1. 01 · Fork

    New isolated microVM on your subdomain — creator agent state included.

  2. 02 · Your keys

    Log into OpenScience with your own model credentials (BYOK).

  3. 03 · Ask it to work

    Open the terminal and give the agent a real job. You keep what ships.

Example first ask

Pull my open PRs, summarize risk, and draft a review checklist.

30-day money-back on your first purchase · no free trial · keys never leave the VM

About this template

Target briefs with accessions you can click

If your biology agent invents proteins and never cites a database, this is the fix. This OpenScience box writes target briefs grounded in UniProt, PDB and ChEMBL — with the identifiers printed, so you can open them and check.

What an AI drug discovery brief looks like here

Ask for a target. Get a sequence-to-structure-to-chemistry brief back.

The workflow runs in that order on purpose. UniProt for the canonical sequence and function. PDB for representative structures. ChEMBL and PubChem for known ligand and bioactivity context. Every claim arrives with its accession attached, because the agent's job is to find and frame the evidence — not to be believed.

The first five minutes

  1. Fork the template, then open Open OpenScience on the VM.
  2. Set one model key in the shell:
export ANTHROPIC_API_KEY=...
  1. Ask the biology agent:

Brief EGFR kinase: UniProt entry, representative PDB structures, and ChEMBL ligands — with accessions I can open.

  1. Open three of the identifiers it hands back.

If the IDs resolve and match what the brief said, the box works. If they don't, you learned that in five minutes instead of five hours.

What lands in your fork

  • A comp-bio oriented biology agent for OpenScience
  • bio-db-query — UniProt, PDB, ChEMBL and PubChem lookups
  • bio-lit-target — literature and target framing
  • The OpenScience browser workspace, reachable through Open OpenScience

What you bring (BYOK)

One model key. ANTHROPIC_API_KEY is the path shown above; OPENAI_API_KEY, GEMINI_API_KEY and OPENROUTER_API_KEY are read too.

Keys stay on your microVM, and the template ships scrubbed — no credentials came across from the box it was captured on. Model spend is yours.

Who this is for

  • Structural and computational biology work you already do by hand
  • You want database-grounded briefs, and you already speak UniProt/PDB/ChEMBL
  • You would rather check an accession than trust a paragraph

Honest limits

It queries public databases and frames literature. That is the whole job.

No docking. No ADMET prediction. No molecular dynamics. No wet-lab LIMS or ELN integration on day one. No ML training loops either — those are the ML co-scientist and inference-lab templates, not this one. And a language model can still misread a record, which is exactly why every brief hands you the accession instead of asking for trust.

Fork it. Ask for EGFR. Click the accessions. Your first jurniti purchase carries a 30-day money-back guarantee.

Inside this fork

Forking copies this template into a brand-new, fully isolated microVM on your own subdomain. Here's exactly what lands in it.

  • OpenScience agent

    The upstream harness, pre-installed — same version the creator ran.

  • Pro VM

    2 vCPU · 6 GiB RAM · 50 GiB disk.

  • Creator's /persist data

    The captured persist volume is copied byte-for-byte into your fork.

  • BYOK — your keys, your VM

    Add your model API keys after forking; they live only inside your microVM.

What this agent can do

2 skills

  • bio-db-query

    UniProt/PDB/ChEMBL/PubChem queries

  • bio-lit-target

    Lit + target framing

What you'll configure after forking

Secrets are scrubbed from shared templates — these are the names you supply in your agent's terminal once it boots.

Environment variables

  • ANTHROPIC_API_KEY
  • GEMINI_API_KEY
  • OPENAI_API_KEY
  • OPENROUTER_API_KEY

Your turn

Your own AI Drug Discovery Agent (UniProt/PDB), live in about 3 minutes.

Copy the setup prompt and build it yourself — or let this exact OpenScienceagent run hosted, already built, on a brand-new, fully isolated microVM on your own subdomain. Add your own keys after it boots; they never leave the box. Don't love it? Your first jurniti purchase comes with 30 days to get every cent back.

New paid VM · BYOK · 30-day money-back on your first purchase · ~3 min to provision

Needs 4 of your own API keys