chemical_patent_analysis

Chemical Patent & Novelty Analysis - Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature. Use this skill for patent chemistry tasks involving get substructure cas get similarity by smiles get compound synonyms by name search literature. Combines 4 tools from 3 SCP server(s).

Best use case

chemical_patent_analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Chemical Patent & Novelty Analysis - Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature. Use this skill for patent chemistry tasks involving get substructure cas get similarity by smiles get compound synonyms by name search literature. Combines 4 tools from 3 SCP server(s).

Teams using chemical_patent_analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/chemical_patent_analysis/SKILL.md --create-dirs "https://raw.githubusercontent.com/SpectrAI-Initiative/InnoClaw/main/.claude/skills/chemical_patent_analysis/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/chemical_patent_analysis/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How chemical_patent_analysis Compares

Feature / Agentchemical_patent_analysisStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Chemical Patent & Novelty Analysis - Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature. Use this skill for patent chemistry tasks involving get substructure cas get similarity by smiles get compound synonyms by name search literature. Combines 4 tools from 3 SCP server(s).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Chemical Patent & Novelty Analysis

**Discipline**: Patent Chemistry | **Tools Used**: 4 | **Servers**: 3

## Description

Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature.

## Tools Used

- **`get_substructure_cas`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`get_similarity_by_smiles`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL`
- **`get_compound_synonyms_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem`
- **`search_literature`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`

## Workflow

1. Search CAS by substructure
2. Search ChEMBL by similarity
3. Get compound synonyms
4. Search patent literature

## Test Case

### Input
```json
{
    "smiles": "c1ccc(-c2ccccc2)cc1",
    "compound_name": "biphenyl"
}
```

### Expected Steps
1. Search CAS by substructure
2. Search ChEMBL by similarity
3. Get compound synonyms
4. Search patent literature

## Usage Example

> **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).

```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
    "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory"
}

async def connect(url, stack):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
    read, write, _ = await stack.enter_async_context(transport)
    ctx = ClientSession(read, write)
    session = await stack.enter_async_context(ctx)
    await session.initialize()
    return session

def parse(result):
    try:
        if hasattr(result, 'content') and result.content:
            c = result.content[0]
            if hasattr(c, 'text'):
                try: return json.loads(c.text)
                except: return c.text
        return str(result)
    except: return str(result)

async def main():
    async with AsyncExitStack() as stack:
        # Connect to required servers
        sessions = {}
        sessions["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack)
        sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)
        sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)

        # Execute workflow steps
        # Step 1: Search CAS by substructure
        result_1 = await sessions["pubchem-server"].call_tool("get_substructure_cas", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Search ChEMBL by similarity
        result_2 = await sessions["chembl-server"].call_tool("get_similarity_by_smiles", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Get compound synonyms
        result_3 = await sessions["pubchem-server"].call_tool("get_compound_synonyms_by_name", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Search patent literature
        result_4 = await sessions["server-1"].call_tool("search_literature", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())
```

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