disease-reversal-prediction

name	disease-reversal-prediction
description	Predict a molecule's ability to reverse disease states using DLEPS (Disease-Ligand Embedding Projection Score) for drug repositioning and discovery.
license	MIT license
metadata	{"skill-author":"PJLab"}

Disease State Reversal Prediction

Usage

1. MCP Server Definition

Use the same DrugSDAClient class as defined in the drug-screening-docking skill.

2. Disease State Reversal Prediction Workflow

This workflow validates SMILES strings and predicts their ability to reverse disease states, useful for drug repositioning and therapeutic discovery.

Workflow Steps:

1. Validate SMILES - Check if input SMILES strings are chemically valid
2. Calculate DLEPS Score - Predict disease state reversal scores for valid molecules

Implementation:

tool_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
model_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model")

if not await tool_client.connect() or not await model_client.connect():
    print("connection failed")
    return

## Input: List of candidate SMILES strings
smiles_list = [
    'Nc1nnc(S(=O)(=O)NCCc2ccc(O)cc2)s1',
    'COc1ccc2c(=O)cc(C(=O)N3CCN(c4ccc(F)cc4)CC3)oc2c1',
    'ABCCOOO'  # Invalid SMILES for demonstration
]

## Step 1: Validate SMILES strings
result = await tool_client.session.call_tool(
    "is_valid_smiles",
    arguments={"smiles_list": smiles_list}
)
result_data = tool_client.parse_result(result)
valid_smiles_list = [x['smiles'] for x in result_data['valid_res'] if x['is_valid'] is True]

print(f"Valid SMILES: {len(valid_smiles_list)}/{len(smiles_list)}")

## Step 2: Calculate DLEPS scores for disease state reversal
disease_name = "Aging"  # Can be: Aging, Alzheimer's, Parkinson's, etc.

result = await model_client.session.call_tool(
    "calculate_dleps_score",
    arguments={
        "smiles_list": valid_smiles_list,
        "disease_name": disease_name
    }
)
result_data = model_client.parse_result(result)

## Display results sorted by score
pred_scores = sorted(result_data['pred_scores'], key=lambda x: x['cs_score'], reverse=True)
for item in pred_scores:
    print(f"SMILES: {item['smiles']}")
    print(f"Disease Reversal Score: {item['cs_score']:.4f}\n")

await tool_client.disconnect()
await model_client.disconnect()

Tool Descriptions

DrugSDA-Tool Server:

• is_valid_smiles: Validate SMILES strings for chemical correctness
  • Args: smiles_list (List[str])
  • Returns: valid_res with is_valid boolean for each SMILES

DrugSDA-Model Server:

• calculate_dleps_score: Predict disease state reversal scores
  • Args: smiles_list (List[str]), disease_name (str)
  • Returns: pred_scores with cs_score (float, 0-1) for each molecule

Input/Output

Input:

• smiles_list: List of SMILES strings to evaluate
• disease_name: Target disease (e.g., "Aging", "Alzheimer's", "Parkinson's")

Output:

• pred_scores: List of dictionaries containing:
  • smiles: Input SMILES string
  • cs_score: Disease reversal score (0-1, higher is better)

Score Interpretation

• cs_score > 0.5: Strong potential for disease state reversal
• cs_score 0.2-0.5: Moderate potential
• cs_score < 0.2: Low potential

Molecules with higher scores are more likely to reverse the disease-associated transcriptional signature.

Supported Diseases

The model supports various diseases including but not limited to:

• Aging
• Alzheimer's Disease
• Parkinson's Disease
• Cardiovascular diseases
• Cancer subtypes
• Inflammatory diseases

Consult the MCP server documentation for the complete list of supported diseases.