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Future House Edison Client Phoenix Chemistry

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Cheminformatics-grounded chemistry agent (Phoenix, the successor to ChemCrow) via the FutureHouse Platform. Use for retrosynthesis, reaction planning, molecular property prediction, SMILES manipulation, and proposing new molecules with chemistry tools backing the reasoning. Trigger on chemistry / drug-design / synthesis / molecule questions.

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  • Added September 11, 2026
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Works with

  • cli
  • api

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Scanned September 11, 2026

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SKILL.md
---
name: phoenix-chemistry
description: Cheminformatics-grounded chemistry agent (Phoenix, the successor to ChemCrow) via the FutureHouse Platform. Use for retrosynthesis, reaction planning, molecular property prediction, SMILES manipulation, and proposing new molecules with chemistry tools backing the reasoning. Trigger on chemistry / drug-design / synthesis / molecule questions.
metadata:
  skill-author: Compiled from FutureHouse edison-client (Apache-2.0)
  upstream: https://github.com/Future-House/edison-client-docs
---

# Phoenix — Chemistry Reasoning Agent (FutureHouse Platform)

Phoenix is the chemistry-specialized FutureHouse agent (job name `MOLECULES`). It is the successor to [ChemCrow](https://github.com/ur-whitelab/chemcrow-public): an LLM agent equipped with cheminformatics tools (RDKit, retrosynthesis planners, property predictors, reaction databases) that reasons about molecules with grounded operations rather than free-form hallucination.

Use this skill when the user asks chemistry- or drug-design-flavored questions:

- Plan a synthesis route to a target molecule
- Propose new molecules satisfying constraints (Lipinski, target activity, etc.)
- Predict properties (logP, solubility, toxicity, etc.) of a SMILES
- Convert / validate / explain SMILES, InChI, IUPAC names
- Suggest reagents, solvents, conditions for a transformation

For pure literature questions about chemistry, use **Crow** instead.
For chemistry *reasoning model* (open-weights), use the sibling **ether0-chemistry-rewards** skill.

## Prerequisites

- `pip install edison-client`
- `EDISON_API_KEY` from <https://platform.edisonscientific.com/profile>

## Minimal usage

```python
import os
from edison_client import EdisonClient, JobNames

client = EdisonClient(api_key=os.environ["EDISON_API_KEY"])

resp = client.run_tasks_until_done({
    "name": JobNames.MOLECULES,
    "query": (
        "Propose a 4-step synthesis of imatinib from commercially available "
        "starting materials. Include reagents, conditions, and a brief "
        "rationale for each step."
    ),
})
print(resp.answer)
```

## Recipes

### Retrosynthesis plan
```python
resp = client.run_tasks_until_done({
    "name": JobNames.MOLECULES,
    "query": "Plan a retrosynthesis for SMILES Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1",
})
```

### Lead optimization brainstorm
```python
resp = client.run_tasks_until_done({
    "name": JobNames.MOLECULES,
    "query": (
        "Starting from aspirin (CC(=O)Oc1ccccc1C(=O)O), propose 5 analogs that "
        "preserve COX inhibition but reduce gastric side effects. Briefly "
        "justify each modification."
    ),
})
```

### Property check before ordering compounds
```python
resp = client.run_tasks_until_done({
    "name": JobNames.MOLECULES,
    "query": (
        "For each of these candidate kinase inhibitors, compute logP, MW, "
        "TPSA, HBD/HBA, rotatable bonds, and rule-of-5 compliance. Flag any "
        "that fail Lipinski:\n"
        "1. CC(C)c1nc(...)\n"
        "2. ..."
    ),
})
```

### Reaction feasibility / condition lookup
```python
resp = client.run_tasks_until_done({
    "name": JobNames.MOLECULES,
    "query": (
        "I want to do a Suzuki coupling between 4-bromopyridine and "
        "phenylboronic acid. What ligand, base, solvent, and temperature "
        "would you recommend, and what side products should I watch for?"
    ),
})
```

## Demo-friendly examples (good first calls)

- "What is the IUPAC name of `CN1C=NC2=C1C(=O)N(C)C(=O)N2C`?" → caffeine
- "Suggest 3 isosteres of a tert-butyl group that would maintain steric bulk but reduce metabolic liability."
- "Predict the logP of ibuprofen from its SMILES and explain the contributing groups."

## Cost / latency

- ~30 s – 4 min depending on whether retrosynthesis tools are invoked
- Credits: typically between Crow and Falcon

## Failure modes / caveats

- Phoenix's predictions are *model-grounded* but not infallible — for safety-critical chemistry (toxicology, regulatory) treat outputs as a hypothesis to verify, not a final answer.
- Very long SMILES with stereochemistry should be sanity-checked with RDKit locally.
- Patent-protected synthesis routes may be missing from the underlying tools' databases.

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