drugsda-target-retrieve
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
Best use case
drugsda-target-retrieve is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
Teams using drugsda-target-retrieve 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/drugsda-target-retrieve/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How drugsda-target-retrieve Compares
| Feature / Agent | drugsda-target-retrieve | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
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
# Target Protein Retrieve
## Usage
### 1. MCP Server Definition
```python
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
)
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
return False
async def disconnect(self):
try:
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
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)}
```
### 2. Retrieve Protein Structure
The description of tool *retrieve_protein_structure_by_gene_name*.
```tex
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures. Note that species support is limited to humans only.
Args:
gene_name (str): Input gene name (e.g., 'TP53')
Return:
status (str): success/error
msg (str): message
prot_structure_path (str): Path to the downloaded protein structure file (pdb format)
```
How to use tool *retrieve_protein_structure_by_gene_name* :
```python
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
response = await client.session.call_tool(
"retrieve_protein_structure_by_gene_name",
arguments={
"gene_name": gene_name
}
)
result = client.parse_result(response)
prot_structure_path = result["prot_structure_path"]
await client.disconnect()
```Related Skills
antibody_target_analysis
Antibody-Target Analysis - Analyze an antibody target: UniProt protein info, InterPro domains, protein properties, and biotherapeutic data from ChEMBL. Use this skill for immunology tasks involving get uniprotkb entry by accession query interpro ComputeProtPara get biotherapeutic by name. Combines 4 tools from 4 SCP server(s).
gene_therapy_target
Gene Therapy Target Analysis - Analyze gene therapy target: gene info, variant pathogenicity, protein structure, and clinical evidence. Use this skill for gene therapy tasks involving get gene metadata by gene name get vep hgvs Protein structure prediction ESMFold clinvar search. Combines 4 tools from 4 SCP server(s).
drug_target_identification
Drug Target Identification Pipeline - Identify drug targets for a disease by querying OpenTargets for associated targets, then retrieve detailed target info from ChEMBL and protein data from UniProt. Use this skill for drug discovery tasks involving get associated targets by disease efoId get target by name get general info by protein or gene name. Combines 3 tools from 3 SCP server(s).
opentargets-disease-target
Retrieve disease-associated targets from Open Targets using disease EFO IDs to identify therapeutic targets.
drugsda-rgroup-sampling
Generate new molecules sampling from the input scaffold.
drugsda-prosst
Given a protein sequence and its structure, employ ProSST model to predict mutation effects and obtain the top-k mutated sequences.
drugsda-peptide-sampling
Generate new peptide molecules sampling from the input peptide sequence.
drugsda-p2rank
No description provided.
drugsda-mol2mol-sampling
Generate new molecules sampling from the input molecule.
drugsda-mol-similarity
Compute the Tanimoto similarities between a target molecule and a list of candidate molecules using Morgan fingerprints.
drugsda-mol-properties
Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively.
drugsda-linker-sampling
Generate new molecules sampling from the input two warhead fragments.