Molecular Property Profiling

from collections import defaultdict

def merge_lists_by_smiles(*lists):
    """Merge multiple descriptor lists by SMILES key"""
    merged = defaultdict(dict)
    for lst in lists:
        for d in lst:
            smiles = d['smiles']
            merged[smiles].update(d)
    return list(merged.values())

client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return

## Input: List of SMILES strings
smiles_list = [
    'Nc1nnc(S(=O)(=O)NCCc2ccc(O)cc2)s1',
    'COc1ccc2c(=O)cc(C(=O)N3CCN(c4ccc(F)cc4)CC3)oc2c1',
    'CCCC1CCC(CC(=O)Cl)(C2CCCCC2)CC1'
]

## Step 1: Calculate basic molecular properties
result = await client.session.call_tool(
    "calculate_mol_basic_info",
    arguments={"smiles_list": smiles_list}
)
basic_metrics = client.parse_result(result)['metrics']

## Step 2: Calculate hydrophobicity descriptors
result = await client.session.call_tool(
    "calculate_mol_hydrophobicity",
    arguments={"smiles_list": smiles_list}
)
hydrophobicity_metrics = client.parse_result(result)['metrics']

## Step 3: Calculate hydrogen bonding properties
result = await client.session.call_tool(
    "calculate_mol_hbond",
    arguments={"smiles_list": smiles_list}
)
hbond_metrics = client.parse_result(result)['metrics']

## Step 4: Calculate structural complexity
result = await client.session.call_tool(
    "calculate_mol_structure_complexity",
    arguments={"smiles_list": smiles_list}
)
structure_metrics = client.parse_result(result)['metrics']

## Step 5: Calculate topological descriptors
result = await client.session.call_tool(
    "calculate_mol_topology",
    arguments={"smiles_list": smiles_list}
)
topology_metrics = client.parse_result(result)['metrics']

## Step 6: Calculate drug chemistry properties
result = await client.session.call_tool(
    "calculate_mol_drug_chemistry",
    arguments={"smiles_list": smiles_list}
)
chemistry_metrics = client.parse_result(result)['metrics']

## Step 7: Calculate charge properties
result = await client.session.call_tool(
    "calculate_mol_charge",
    arguments={"smiles_list": smiles_list}
)
charge_metrics = client.parse_result(result)['metrics']

## Step 8: Calculate complexity metrics
result = await client.session.call_tool(
    "calculate_mol_complexity",
    arguments={"smiles_list": smiles_list}
)
complexity_metrics = client.parse_result(result)['metrics']

## Merge all descriptors by SMILES
complete_profiles = merge_lists_by_smiles(
    basic_metrics,
    hydrophobicity_metrics,
    hbond_metrics,
    structure_metrics,
    topology_metrics,
    chemistry_metrics,
    charge_metrics,
    complexity_metrics
)

## Display results
for profile in complete_profiles:
    print(f"\nSMILES: {profile['smiles']}")
    print(f"Molecular Formula: {profile['molecular_formula']}")
    print(f"Molecular Weight: {profile['molecular_weight']:.2f}")
    print(f"LogP: {profile['logp']:.2f}")
    print(f"QED Score: {profile['qed']:.4f}")
    print(f"H-Bond Donors: {profile['num_h_donors']}")
    print(f"H-Bond Acceptors: {profile['num_h_acceptors']}")
    print(f"TPSA: {profile['tpsa']:.2f}")
    print(f"Lipinski Violations: {profile['lipinski_rule_of_5_violations']}")

await client.disconnect()