Quantum Pharmaceutical Optimization System

from qpharos.simple import quick_dock

# Dock aspirin to COX-2 receptor
affinity = quick_dock(
    'CC(=O)Oc1ccccc1C(=O)O',  # Aspirin SMILES
    '1CVU'                     # COX-2 PDB ID
)

print(f"Binding Affinity: {affinity:.2f} kcal/mol")
# Runs on IBM Torino with 2000 shots and QEC

from qpharos import dock, predict_admet
import os

api_key = os.getenv('BIOQL_API_KEY')

# Docking with custom parameters
result = dock(
    ligand='CC(=O)Oc1ccccc1C(=O)O',
    receptor='1CVU',
    api_key=api_key,
    backend='ibm_torino',  # 133 qubits
    shots=5000,            # More shots = better accuracy
    qec=True              # Quantum error correction
)

print(f"Binding Affinity: {result.binding_affinity:.2f} kcal/mol")
print(f"Ki: {result.ki:.2f} nM")
print(f"IC50: {result.ic50:.2f} nM")

# ADMET prediction
admet = predict_admet(
    smiles='CC(=O)Oc1ccccc1C(=O)O',
    api_key=api_key,
    backend='ibm_torino',
    shots=1500
)

print(f"Lipinski Pass: {admet.lipinski_pass}")
print(f"QED Score: {admet.qed_score:.2f}")
print(f"BBB Permeability: {admet.bbb_permeability}")

from qpharos import QPHAROSAnalyzer, QPHAROSConfig
import os

# Configure analyzer
config = QPHAROSConfig(
    backend='ibm_torino',
    shots=2000,
    api_key=os.getenv('BIOQL_API_KEY'),
    qec_enabled=True
)

# Create analyzer with session management
analyzer = QPHAROSAnalyzer(config)

# Screen compound library
ligands = [
    'CCO',                    # Ethanol
    'CC(=O)Oc1ccccc1C(=O)O',  # Aspirin
    'CN1C=NC2=C1C(=O)N(C(=O)N2C)C'  # Caffeine
]

hits = analyzer.screen(
    ligands=ligands,
    receptor='6B3J',
    affinity_threshold=-7.0  # Only strong binders
)

print(f"Screened {len(ligands)} compounds")
print(f"Found {len(hits)} hits")

# Full pipeline for best hit
if hits:
    result = analyzer.full_pipeline(hits[0].ligand_smiles, '6B3J')
    print(f"Best hit recommendation: {result['recommendation']}")

# Generate reports
analyzer.save_report('screening_results.json', format='json')
analyzer.save_report('screening_results.txt', format='text')

# Session summary
summary = analyzer.get_session_summary()
print(f"Session ID: {summary['session_id']}")
print(f"Total analyses: {summary['total_analyses']}")

from qpharos.layers import (
    QuantumFeatureEncoder,
    QuantumEntanglementMapper,
    QuantumConformationalSearch,
    QuantumScoringFunction,
    QuantumErrorCorrection
)
from qpharos.core import quantum_backend
import os

# Initialize quantum backend
backend = quantum_backend.QuantumBackend(
    backend_name='ibm_torino',
    shots=2000,
    api_key=os.getenv('BIOQL_API_KEY')
)

print("Running 5-Layer QPHAROS Architecture...")

# Layer 1: Quantum Feature Encoding
encoder = QuantumFeatureEncoder(backend)
ligand_encoded = encoder.encode_molecule('CCO')
receptor_encoded = encoder.encode_protein('6B3J')
print(f"Layer 1: Ligand qubits = {ligand_encoded.n_qubits}")

# Layer 2: Quantum Entanglement Mapping
mapper = QuantumEntanglementMapper(backend)
entangled_state = mapper.create_entanglement(ligand_encoded, receptor_encoded)
print(f"Layer 2: Circuit depth = {entangled_state.circuit_depth}")

# Layer 3: Quantum Conformational Search (QAOA)
search = QuantumConformationalSearch(backend)
conformers = search.find_conformers(entangled_state, n_conformers=5)
print(f"Layer 3: Found {len(conformers)} conformers")

# Layer 4: Quantum Scoring Function (VQE)
scorer = QuantumScoringFunction(backend)
affinity = scorer.calculate_binding_affinity(conformers[0])
print(f"Layer 4: Binding Affinity = {affinity:.2f} kcal/mol")

# Layer 5: Quantum Error Correction
qec = QuantumErrorCorrection(backend, code_type='surface_code')
corrected_affinity = qec.correct_measurement(affinity)
print(f"Layer 5: QEC Corrected = {corrected_affinity:.2f} kcal/mol")
print(f"Error margin: {abs(corrected_affinity - affinity):.4f} kcal/mol")