Menu
Analyze chemical structures from compound names to retrieve SMILES, molecular formulas, molecular weight, and LogP values.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | chemical-structure-analysis |
| description | Analyze chemical structures from compound names to retrieve SMILES, molecular formulas, molecular weight, and LogP values. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
import asyncio
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class InternAgentClient:
"""InternAgent MCP Client"""
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": self.api_key}
)
self._stack = AsyncExitStack()
await self._stack.__aenter__()
self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self._stack.enter_async_context(self.session_ctx)
await self.session.initialize()
return True
except Exception as e:
print(f"✗ connect failure: {e}")
return False
async def disconnect(self):
"""Disconnect from server"""
try:
if hasattr(self, '_stack'):
await self._stack.aclose()
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}
Analyze chemical structures from compound names.
Implementation:
## Initialize client
client = InternAgentClient(
"https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
"<your-api-key>"
)
if not await client.connect():
print("connection failed")
exit()
## Input: Compound name
compound_name = "aspirin"
## Analyze chemical structure
result = await client.session.call_tool(
"ChemicalStructureAnalyzer",
arguments={
"compound_name": compound_name
}
)
data = client.parse_result(result)
print(f"Compound: {compound_name}")
print(f"SMILES: {data.get('smiles', 'N/A')}")
print(f"Molecular Formula: {data.get('molecular_formula', 'N/A')}")
print(f"Molecular Weight: {data.get('molecular_weight', 'N/A')}")
print(f"LogP: {data.get('logP', 'N/A')}")
await client.disconnect()
InternAgent Server:
ChemicalStructureAnalyzer: Analyze chemical structure from compound name
compound_name (str): Chemical compound namesmiles (str): SMILES notationmolecular_formula (str): Chemical formulamolecular_weight (float): Molecular weight (g/mol)logP (float): Partition coefficientUse the local InnoClaw CLI to run app workflows and Deep Research sessions from the terminal. Trigger when the user wants command-line control over this repository instead of only using the web UI.
Use when the user wants the full multi-stage paper discussion workflow, a comprehensive structured discussion of one paper, or an equivalent full-discussion request in another language.
Use when the user wants question-driven help on one paper, including paper understanding, follow-up questions, method or experiment clarification, limitation analysis, or comparison against related work in any language.
Use when the user wants the full research ideation workflow grounded in one seed paper, including complete ideation, feasibility review, experiment planning, and final synthesis, or makes an equivalent ideation request in another language.
ADMET & Drug-Likeness Report - Generate comprehensive ADMET and drug-likeness report: molecular properties, H-bond analysis, hydrophobicity, topology, and ADMET prediction. Use this skill for medicinal chemistry tasks involving calculate mol basic info calculate mol hbond calculate mol hydrophobicity calculate mol topology pred molecule admet. Combines 5 tools from 2 SCP server(s).
Affinity Maturation Pipeline - Affinity maturation: compute binding affinity, predict mutations, compute hydrophilicity, and predict drug-target interaction. Use this skill for antibody engineering tasks involving ComputeAffinityCalculator zero shot sequence prediction ComputeHydrophilicity PredictDrugTargetInteraction. Combines 4 tools from 3 SCP server(s).