Drug Discovery with Quantum Entangled Knowledge Graphs¶
This comprehensive use case demonstrates how QE-KGR revolutionizes pharmaceutical research by modeling molecular interactions, drug-target relationships, and biological pathways as quantum entangled systems.
🧬 Overview¶
Drug discovery traditionally relies on classical computational methods that struggle to capture the complex, non-linear relationships between molecules, targets, and biological systems. QE-KGR introduces quantum mechanics principles to model:
- Quantum Superposition: Drugs and targets existing in multiple states simultaneously
- Entanglement: Non-classical correlations between molecular entities
- Interference: Constructive/destructive effects in drug combinations
- Quantum Walks: Enhanced exploration of molecular interaction networks
🎯 Key Applications¶
1. Drug Repurposing Discovery¶
2. Novel Target Identification¶
3. Drug-Drug Interaction Prediction¶
4. Molecular Network Analysis¶
5. Biomarker Discovery¶
🧪 Complete Drug Discovery Example¶
Let's build a comprehensive quantum knowledge graph for drug discovery:
import numpy as np
import qekgr
from qekgr import EntangledGraph, QuantumInference, EntangledQueryEngine
from qekgr.utils import QuantumGraphVisualizer
def create_comprehensive_drug_graph():
"""Create a comprehensive quantum drug discovery knowledge graph."""
# Use higher dimensional Hilbert space for complex molecular states
graph = EntangledGraph(hilbert_dim=8)
# === ADD DRUG MOLECULES ===
drugs = [
# Anti-inflammatory drugs
("Aspirin", "anti_inflammatory", {
"target": ["COX1", "COX2"],
"indication": ["pain", "inflammation", "fever"],
"mechanism": "COX_inhibition",
"bioavailability": 0.8,
"half_life": 4.0
}),
("Ibuprofen", "anti_inflammatory", {
"target": ["COX1", "COX2"],
"indication": ["pain", "inflammation"],
"mechanism": "COX_inhibition",
"bioavailability": 0.9,
"half_life": 2.5
}),
("Celecoxib", "anti_inflammatory", {
"target": ["COX2"],
"indication": ["arthritis", "pain"],
"mechanism": "selective_COX2_inhibition",
"bioavailability": 0.75,
"half_life": 11.0
}),
# Diabetes medications
("Metformin", "antidiabetic", {
"target": ["AMPK", "Complex_I"],
"indication": ["diabetes_T2", "PCOS"],
"mechanism": "AMPK_activation",
"bioavailability": 0.55,
"half_life": 6.5
}),
("Insulin", "antidiabetic", {
"target": ["Insulin_receptor"],
"indication": ["diabetes_T1", "diabetes_T2"],
"mechanism": "glucose_uptake",
"bioavailability": 1.0,
"half_life": 1.0
}),
("Glimepiride", "antidiabetic", {
"target": ["KATP_channels"],
"indication": ["diabetes_T2"],
"mechanism": "insulin_secretion",
"bioavailability": 1.0,
"half_life": 5.0
}),
# Cardiovascular drugs
("Atorvastatin", "statin", {
"target": ["HMG_CoA_reductase"],
"indication": ["hypercholesterolemia", "CAD_prevention"],
"mechanism": "cholesterol_synthesis_inhibition",
"bioavailability": 0.14,
"half_life": 14.0
}),
("Lisinopril", "ACE_inhibitor", {
"target": ["ACE"],
"indication": ["hypertension", "heart_failure"],
"mechanism": "angiotensin_conversion_inhibition",
"bioavailability": 0.25,
"half_life": 12.0
}),
("Warfarin", "anticoagulant", {
"target": ["VKORC1", "CYP2C9"],
"indication": ["thrombosis", "atrial_fibrillation"],
"mechanism": "vitamin_K_cycle_inhibition",
"bioavailability": 1.0,
"half_life": 40.0
}),
# Experimental/investigational
("Compound_X", "experimental", {
"target": ["Novel_target_1"],
"indication": ["neurodegeneration"],
"mechanism": "unknown",
"bioavailability": 0.6,
"half_life": 8.0
})
]
for drug_name, drug_class, metadata in drugs:
graph.add_quantum_node(drug_name, state=drug_class, metadata=metadata)
# === ADD PROTEIN TARGETS ===
targets = [
("COX1", "enzyme", {
"function": "prostaglandin_synthesis",
"location": ["stomach", "platelets"],
"pathway": "arachidonic_acid",
"essentiality": "essential",
"druggability": 0.8
}),
("COX2", "enzyme", {
"function": "inflammation_mediation",
"location": ["inflammatory_sites", "brain"],
"pathway": "arachidonic_acid",
"essentiality": "induced",
"druggability": 0.9
}),
("AMPK", "kinase", {
"function": "energy_homeostasis",
"location": ["liver", "muscle", "adipose"],
"pathway": ["glucose_metabolism", "lipid_metabolism"],
"essentiality": "critical",
"druggability": 0.7
}),
("HMG_CoA_reductase", "enzyme", {
"function": "cholesterol_synthesis",
"location": ["liver"],
"pathway": "mevalonate",
"essentiality": "important",
"druggability": 0.95
}),
("ACE", "enzyme", {
"function": "blood_pressure_regulation",
"location": ["lungs", "kidneys"],
"pathway": "renin_angiotensin",
"essentiality": "important",
"druggability": 0.85
}),
("VKORC1", "enzyme", {
"function": "vitamin_K_recycling",
"location": ["liver"],
"pathway": "coagulation_cascade",
"essentiality": "critical",
"druggability": 0.7
}),
("Insulin_receptor", "receptor", {
"function": "glucose_signaling",
"location": ["muscle", "liver", "adipose"],
"pathway": "insulin_signaling",
"essentiality": "critical",
"druggability": 0.6
}),
("Novel_target_1", "unknown", {
"function": "neuroprotection",
"location": ["brain"],
"pathway": "unknown",
"essentiality": "unknown",
"druggability": 0.5
})
]
for target_name, target_type, metadata in targets:
graph.add_quantum_node(target_name, state=target_type, metadata=metadata)
# === ADD DISEASES/CONDITIONS ===
conditions = [
("Pain", "symptom", {
"category": "sensory",
"severity_range": [1, 10],
"affected_pathways": ["nociception", "inflammation"]
}),
("Inflammation", "process", {
"category": "immune_response",
"type": "pathological",
"biomarkers": ["CRP", "IL6", "TNF_alpha"]
}),
("Type2_Diabetes", "metabolic_disease", {
"category": "endocrine",
"prevalence": 0.11,
"biomarkers": ["HbA1c", "glucose", "insulin"]
}),
("Cardiovascular_Disease", "systemic_disease", {
"category": "circulatory",
"prevalence": 0.06,
"biomarkers": ["LDL", "HDL", "CRP", "troponin"]
}),
("Hypertension", "circulatory_condition", {
"category": "pressure_disorder",
"prevalence": 0.45,
"biomarkers": ["BP_systolic", "BP_diastolic"]
}),
("Thrombosis", "blood_disorder", {
"category": "coagulation",
"severity": "severe",
"biomarkers": ["D_dimer", "PT", "INR"]
})
]
for condition_name, condition_type, metadata in conditions:
graph.add_quantum_node(condition_name, state=condition_type, metadata=metadata)
# === ADD BIOLOGICAL PATHWAYS ===
pathways = [
("Arachidonic_Acid_Pathway", "metabolic_pathway", {
"key_enzymes": ["COX1", "COX2", "LOX"],
"products": ["PGE2", "PGI2", "TXA2"],
"regulation": "inflammatory_stimuli"
}),
("Insulin_Signaling", "signaling_pathway", {
"key_proteins": ["Insulin_receptor", "IRS1", "PI3K", "AKT"],
"outcome": "glucose_uptake",
"diseases": ["diabetes", "metabolic_syndrome"]
}),
("Cholesterol_Synthesis", "biosynthetic_pathway", {
"key_enzymes": ["HMG_CoA_reductase", "squalene_synthase"],
"regulation": "feedback_inhibition",
"diseases": ["hypercholesterolemia"]
})
]
for pathway_name, pathway_type, metadata in pathways:
graph.add_quantum_node(pathway_name, state=pathway_type, metadata=metadata)
# === CREATE QUANTUM ENTANGLED RELATIONSHIPS ===
# Drug-Target interactions with quantum superposition
drug_target_interactions = [
("Aspirin", "COX1", ["inhibits", "acetylates", "irreversible_binding"], [0.9, 0.8, 0.85]),
("Aspirin", "COX2", ["inhibits", "acetylates"], [0.7, 0.6]),
("Ibuprofen", "COX1", ["inhibits", "competitive"], [0.7, 0.8]),
("Ibuprofen", "COX2", ["inhibits", "selective"], [0.9, 0.85]),
("Celecoxib", "COX2", ["inhibits", "highly_selective"], [0.95, 0.9]),
("Metformin", "AMPK", ["activates", "allosteric_binding"], [0.9, 0.8]),
("Atorvastatin", "HMG_CoA_reductase", ["inhibits", "competitive"], [0.95, 0.9]),
("Lisinopril", "ACE", ["inhibits", "active_site_binding"], [0.9, 0.85]),
("Warfarin", "VKORC1", ["inhibits", "interferes"], [0.8, 0.7]),
("Insulin", "Insulin_receptor", ["activates", "hormone_binding"], [1.0, 0.95]),
("Compound_X", "Novel_target_1", ["modulates", "unknown_mechanism"], [0.6, 0.4])
]
for drug, target, relations, amplitudes in drug_target_interactions:
graph.add_entangled_edge(drug, target, relations, amplitudes)
# Drug-Condition relationships
drug_condition_relations = [
("Aspirin", "Pain", ["treats", "reduces", "analgesic"], [0.8, 0.7, 0.75]),
("Aspirin", "Inflammation", ["reduces", "anti_inflammatory"], [0.7, 0.8]),
("Ibuprofen", "Pain", ["treats", "stronger_than_aspirin"], [0.9, 0.8]),
("Ibuprofen", "Inflammation", ["reduces", "potent_anti_inflammatory"], [0.85, 0.9]),
("Celecoxib", "Inflammation", ["reduces", "selective_action"], [0.9, 0.85]),
("Metformin", "Type2_Diabetes", ["treats", "first_line", "glucose_control"], [0.9, 0.95, 0.85]),
("Insulin", "Type2_Diabetes", ["treats", "glucose_control"], [0.95, 0.9]),
("Atorvastatin", "Cardiovascular_Disease", ["prevents", "lipid_control"], [0.8, 0.85]),
("Lisinopril", "Hypertension", ["treats", "ACE_inhibition"], [0.9, 0.85]),
("Lisinopril", "Cardiovascular_Disease", ["prevents", "cardioprotective"], [0.75, 0.7]),
("Warfarin", "Thrombosis", ["prevents", "anticoagulation"], [0.9, 0.85])
]
for drug, condition, relations, amplitudes in drug_condition_relations:
graph.add_entangled_edge(drug, condition, relations, amplitudes)
# Target-Condition relationships
target_condition_relations = [
("COX1", "Pain", ["mediates", "peripheral_sensitization"], [0.7, 0.6]),
("COX2", "Inflammation", ["drives", "inflammatory_response"], [0.9, 0.85]),
("AMPK", "Type2_Diabetes", ["regulates", "glucose_homeostasis"], [0.8, 0.75]),
("HMG_CoA_reductase", "Cardiovascular_Disease", ["contributes", "cholesterol_elevation"], [0.7, 0.8]),
("ACE", "Hypertension", ["mediates", "blood_pressure_elevation"], [0.85, 0.8]),
("VKORC1", "Thrombosis", ["prevents_when_active", "coagulation_cascade"], [0.7, 0.75]),
("Insulin_receptor", "Type2_Diabetes", ["dysfunction_causes", "insulin_resistance"], [0.9, 0.85])
]
for target, condition, relations, amplitudes in target_condition_relations:
graph.add_entangled_edge(target, condition, relations, amplitudes)
# Target-Pathway relationships
target_pathway_relations = [
("COX1", "Arachidonic_Acid_Pathway", ["key_enzyme", "prostaglandin_synthesis"], [0.9, 0.85]),
("COX2", "Arachidonic_Acid_Pathway", ["key_enzyme", "inflammatory_prostaglandins"], [0.9, 0.9]),
("Insulin_receptor", "Insulin_Signaling", ["initiates", "signal_transduction"], [0.95, 0.9]),
("HMG_CoA_reductase", "Cholesterol_Synthesis", ["rate_limiting", "mevalonate_formation"], [0.95, 0.9])
]
for target, pathway, relations, amplitudes in target_pathway_relations:
graph.add_entangled_edge(target, pathway, relations, amplitudes)
# Drug-Drug interactions (potential combinations/contraindications)
drug_drug_interactions = [
("Aspirin", "Warfarin", ["potentiates", "bleeding_risk"], [0.8, 0.9]),
("Ibuprofen", "Lisinopril", ["antagonizes", "reduces_efficacy"], [0.7, 0.6]),
("Metformin", "Insulin", ["synergistic", "additive_glucose_control"], [0.8, 0.75]),
("Atorvastatin", "Warfarin", ["interacts", "CYP_competition"], [0.6, 0.5])
]
for drug1, drug2, relations, amplitudes in drug_drug_interactions:
graph.add_entangled_edge(drug1, drug2, relations, amplitudes)
return graph
def discover_drug_repurposing_opportunities(graph, inference_engine, query_engine):
"""Use quantum reasoning to discover drug repurposing opportunities."""
print("🔬 Quantum Drug Repurposing Discovery")
print("=" * 50)
repurposing_queries = [
"What diabetes drugs might help with inflammation?",
"Could anti-inflammatory drugs treat cardiovascular disease?",
"What drugs target pathways connected to neurodegeneration?",
"Find drugs with multi-target potential for combination therapy",
"Which cardiovascular drugs might have anti-diabetic effects?"
]
discoveries = []
for query in repurposing_queries:
print(f"\n🔍 Query: {query}")
results = query_engine.query(query, max_results=3)
for i, result in enumerate(results, 1):
if result.confidence_score > 0.25:
print(f" Result {i} (Confidence: {result.confidence_score:.3f}):")
print(f" Entities: {', '.join(result.answer_nodes)}")
print(f" Reasoning: {' → '.join(result.reasoning_path)}")
discoveries.append({
'query': query,
'entities': result.answer_nodes,
'confidence': result.confidence_score,
'reasoning': result.reasoning_path
})
return discoveries
def predict_novel_drug_targets(graph, inference_engine):
"""Use quantum walks to discover novel drug targets."""
print("\n🎯 Novel Drug Target Discovery via Quantum Walks")
print("=" * 55)
# Start quantum walks from known effective drugs
effective_drugs = ["Metformin", "Atorvastatin", "Lisinopril"]
novel_targets = {}
for drug in effective_drugs:
print(f"\n🚶 Quantum walk from {drug}:")
# Perform quantum walk with bias toward protein targets
walk_result = inference_engine.quantum_walk(
start_node=drug,
steps=12,
bias_relations=["inhibits", "activates", "modulates", "binds"]
)
print(f" Walk path: {' → '.join(walk_result.path[:8])}...") # Show first 8 steps
print(f" Final quantum amplitudes: {np.abs(walk_result.final_state)[:5]}")
print(f" Entanglement evolution: {[f'{e:.3f}' for e in walk_result.entanglement_trace[:6]]}")
# Analyze path for potential new targets
targets_in_path = []
for node in walk_result.path:
if node in graph.nodes:
node_metadata = graph.nodes[node].metadata
if node_metadata.get('function') and 'druggability' in node_metadata:
druggability = node_metadata['druggability']
if druggability > 0.6 and node not in [drug]: # Exclude starting drug
targets_in_path.append((node, druggability))
# Rank by druggability and quantum amplitude
if targets_in_path:
novel_targets[drug] = sorted(targets_in_path, key=lambda x: x[1], reverse=True)[:3]
return novel_targets
def analyze_drug_drug_interactions(graph, inference_engine):
"""Predict drug-drug interactions using quantum entanglement analysis."""
print("\n⚠️ Drug-Drug Interaction Analysis")
print("=" * 40)
drugs = [node for node in graph.nodes.keys()
if 'anti_inflammatory' in str(graph.nodes[node].metadata.get('mechanism', '')) or
'antidiabetic' in str(graph.nodes[node].state_vector) or
'statin' in str(graph.nodes[node].state_vector)]
interactions = []
for i, drug1 in enumerate(drugs):
for drug2 in drugs[i+1:]:
# Find quantum pathways connecting drugs
drug1_neighbors = set(graph.get_neighbors(drug1))
drug2_neighbors = set(graph.get_neighbors(drug2))
# Shared targets indicate potential interaction
shared_targets = drug1_neighbors & drug2_neighbors
if shared_targets:
# Calculate quantum interference between drug states
overlap = graph.get_quantum_state_overlap(drug1, drug2)
interaction_strength = abs(overlap)
# Get drug metadata for interaction analysis
drug1_meta = graph.nodes[drug1].metadata
drug2_meta = graph.nodes[drug2].metadata
print(f"\n {drug1} ↔ {drug2}:")
print(f" Shared targets: {', '.join(shared_targets)}")
print(f" Quantum overlap: {interaction_strength:.3f}")
print(f" Drug1 half-life: {drug1_meta.get('half_life', 'unknown')}h")
print(f" Drug2 half-life: {drug2_meta.get('half_life', 'unknown')}h")
# Determine interaction type
if interaction_strength > 0.7:
interaction_type = "⚠️ HIGH risk interaction"
elif interaction_strength > 0.4:
interaction_type = "⚡ Moderate synergy potential"
else:
interaction_type = "✅ Low interaction risk"
print(f" Assessment: {interaction_type}")
interactions.append({
'drug1': drug1,
'drug2': drug2,
'shared_targets': list(shared_targets),
'interaction_strength': interaction_strength,
'assessment': interaction_type
})
return interactions
def discover_molecular_networks(graph, inference_engine):
"""Discover entangled molecular interaction networks."""
print("\n🕸️ Molecular Network Discovery")
print("=" * 35)
# Define seed combinations for network discovery
network_seeds = [
(["COX1", "COX2"], "Inflammation Network"),
(["AMPK", "Insulin_receptor"], "Metabolic Network"),
(["ACE", "VKORC1"], "Cardiovascular Network"),
(["HMG_CoA_reductase", "Arachidonic_Acid_Pathway"], "Lipid-Inflammation Network")
]
discovered_networks = {}
for seeds, network_name in network_seeds:
print(f"\n🔬 Discovering {network_name}...")
print(f" Seed nodes: {', '.join(seeds)}")
# Use quantum subgraph discovery
network = inference_engine.discover_entangled_subgraph(
seed_nodes=seeds,
expansion_steps=4,
min_entanglement=0.3
)
print(f" Network nodes ({len(network.nodes)}): {', '.join(list(network.nodes)[:10])}")
if len(network.nodes) > 10:
print(f" ... and {len(network.nodes)-10} more")
print(f" Network density: {network.entanglement_density:.3f}")
print(f" Quantum coherence: {network.coherence_measure:.3f}")
print(f" Discovery confidence: {network.discovery_confidence:.3f}")
# Analyze network for drug development insights
drugs_in_network = [node for node in network.nodes
if node in graph.nodes and
any(drug_class in str(graph.nodes[node].state_vector)
for drug_class in ['anti_inflammatory', 'antidiabetic', 'statin'])]
targets_in_network = [node for node in network.nodes
if node in graph.nodes and
graph.nodes[node].metadata.get('druggability', 0) > 0.7]
print(f" Drugs in network: {drugs_in_network}")
print(f" High-druggability targets: {targets_in_network}")
discovered_networks[network_name] = {
'network': network,
'drugs': drugs_in_network,
'targets': targets_in_network
}
return discovered_networks
def generate_drug_development_insights(graph, repurposing, novel_targets, interactions, networks):
"""Generate actionable insights for drug development."""
print("\n💡 Drug Development Insights & Recommendations")
print("=" * 55)
insights = {
'high_priority_repurposing': [],
'novel_target_opportunities': [],
'safety_concerns': [],
'combination_opportunities': [],
'research_directions': []
}
# Analyze repurposing opportunities
high_confidence_repurposing = [r for r in repurposing if r['confidence'] > 0.4]
if high_confidence_repurposing:
print("\n🔄 High-Priority Drug Repurposing Opportunities:")
for opportunity in high_confidence_repurposing[:3]:
entities = opportunity['entities']
confidence = opportunity['confidence']
print(f" • {', '.join(entities)} (Confidence: {confidence:.3f})")
insights['high_priority_repurposing'].append(opportunity)
# Analyze novel targets
print("\n🎯 Novel Target Development Priorities:")
for drug, targets in novel_targets.items():
if targets:
best_target, druggability = targets[0]
print(f" • {drug} → {best_target} (Druggability: {druggability:.2f})")
insights['novel_target_opportunities'].append({
'source_drug': drug,
'target': best_target,
'druggability': druggability
})
# Safety analysis
high_risk_interactions = [i for i in interactions if i['interaction_strength'] > 0.7]
if high_risk_interactions:
print("\n⚠️ Safety Concerns - High-Risk Drug Combinations:")
for interaction in high_risk_interactions:
print(f" • {interaction['drug1']} + {interaction['drug2']}: {interaction['assessment']}")
insights['safety_concerns'].append(interaction)
# Combination opportunities
synergistic_interactions = [i for i in interactions
if 0.4 < i['interaction_strength'] < 0.7]
if synergistic_interactions:
print("\n⚡ Potential Combination Therapies:")
for interaction in synergistic_interactions:
print(f" • {interaction['drug1']} + {interaction['drug2']}: Synergistic potential")
insights['combination_opportunities'].append(interaction)
# Research directions from network analysis
print("\n🔬 Strategic Research Directions:")
for network_name, network_data in networks.items():
network_obj = network_data['network']
if network_obj.discovery_confidence > 0.5:
print(f" • {network_name}: High-confidence molecular interactions")
print(f" - Coherence: {network_obj.coherence_measure:.3f}")
print(f" - Druggable targets: {len(network_data['targets'])}")
insights['research_directions'].append({
'network': network_name,
'confidence': network_obj.discovery_confidence,
'targets': network_data['targets']
})
return insights
def main():
"""Execute comprehensive drug discovery analysis."""
print("🧬 Quantum-Enhanced Drug Discovery Platform")
print("=" * 50)
print("Modeling molecular interactions as quantum entangled systems...\n")
# Create comprehensive drug discovery graph
print("📊 Building quantum drug discovery knowledge graph...")
drug_graph = create_comprehensive_drug_graph()
print(f"✅ Graph constructed:")
print(f" • {len(drug_graph.nodes)} quantum entities")
print(f" • {len(drug_graph.edges)} entangled relationships")
print(f" • {drug_graph.hilbert_dim}D Hilbert space")
print(f" • Total entanglement: {drug_graph.measure_total_entanglement():.3f}")
# Initialize quantum reasoning engines
print("\n🧠 Initializing quantum reasoning engines...")
inference_engine = QuantumInference(drug_graph)
query_engine = EntangledQueryEngine(drug_graph)
# Execute drug discovery analyses
print("\n🔍 Executing quantum drug discovery protocols...")
repurposing_discoveries = discover_drug_repurposing_opportunities(
drug_graph, inference_engine, query_engine)
novel_targets = predict_novel_drug_targets(drug_graph, inference_engine)
drug_interactions = analyze_drug_drug_interactions(drug_graph, inference_engine)
molecular_networks = discover_molecular_networks(drug_graph, inference_engine)
# Generate insights and recommendations
insights = generate_drug_development_insights(
drug_graph, repurposing_discoveries, novel_targets, drug_interactions, molecular_networks)
# Summary report
print("\n📊 QUANTUM DRUG DISCOVERY SUMMARY REPORT")
print("=" * 45)
print(f"📈 Repurposing opportunities identified: {len(repurposing_discoveries)}")
print(f"🎯 Novel target suggestions: {sum(len(targets) for targets in novel_targets.values())}")
print(f"⚠️ Drug interaction predictions: {len(drug_interactions)}")
print(f"🕸️ Molecular networks discovered: {len(molecular_networks)}")
print(f"💡 High-priority insights generated: {len(insights['high_priority_repurposing'])}")
# Highlight top discovery
if repurposing_discoveries:
top_discovery = max(repurposing_discoveries, key=lambda x: x['confidence'])
print(f"\n🌟 TOP DISCOVERY:")
print(f" Query: {top_discovery['query']}")
print(f" Entities: {', '.join(top_discovery['entities'])}")
print(f" Quantum Confidence: {top_discovery['confidence']:.3f}")
print(f" Reasoning: {' → '.join(top_discovery['reasoning'])}")
# Generate visualizations
try:
print("\n📈 Generating quantum visualizations...")
visualizer = QuantumGraphVisualizer(drug_graph)
# 3D molecular network
fig_3d = visualizer.visualize_graph_3d(
color_by="entanglement",
highlight_nodes=list(novel_targets.keys())
)
fig_3d.write_html("drug_discovery_quantum_network.html")
# Entanglement heatmap
fig_heatmap = visualizer.visualize_entanglement_heatmap()
fig_heatmap.write_html("drug_entanglement_matrix.html")
# Quantum state projections
fig_projection = visualizer.visualize_quantum_states_2d(method="tsne")
fig_projection.write_html("molecular_quantum_states.html")
print("✅ Visualizations saved:")
print(" • drug_discovery_quantum_network.html")
print(" • drug_entanglement_matrix.html")
print(" • molecular_quantum_states.html")
except ImportError:
print("📊 Install plotly for advanced visualizations: pip install plotly")
# Return complete analysis
return {
'graph': drug_graph,
'repurposing': repurposing_discoveries,
'novel_targets': novel_targets,
'interactions': drug_interactions,
'networks': molecular_networks,
'insights': insights
}
if __name__ == "__main__":
# Execute quantum drug discovery analysis
results = main()
print("\n🎉 Quantum drug discovery analysis complete!")
print("🔬 Ready for experimental validation and clinical translation.")
🎯 Key Quantum Advantages¶
1. Superposition Modeling¶
- Drugs can exist in multiple mechanism states simultaneously
- Captures uncertainty in drug-target interactions
- Models polypharmacology naturally
2. Entanglement Correlations¶
- Non-classical relationships between distant molecular entities
- Captures long-range biological network effects
- Enables holistic system-level reasoning
3. Quantum Interference¶
- Constructive interference identifies synergistic combinations
- Destructive interference predicts antagonistic effects
- Optimizes drug combination therapies
4. Enhanced Exploration¶
- Quantum walks explore molecular space more efficiently
- Discovers non-obvious drug-target connections
- Accelerates novel target identification
🔬 Experimental Validation Workflow¶
Phase 1: Computational Validation¶
def validate_quantum_predictions(predictions, known_database):
"""Validate quantum predictions against known drug interactions."""
validation_results = []
for prediction in predictions:
# Check against known databases (ChEMBL, DrugBank, etc.)
known_interactions = query_drugbank(prediction['entities'])
# Calculate validation metrics
precision = calculate_precision(prediction, known_interactions)
recall = calculate_recall(prediction, known_interactions)
validation_results.append({
'prediction': prediction,
'precision': precision,
'recall': recall,
'validation_status': 'confirmed' if precision > 0.7 else 'novel'
})
return validation_results
Phase 2: In Vitro Testing¶
- Molecular Docking: Validate predicted drug-target interactions
- Biochemical Assays: Measure binding affinities and IC50 values
- Cell-based Assays: Test functional effects in relevant cell lines
Phase 3: In Vivo Studies¶
- Animal Models: Test safety and efficacy in disease models
- Pharmacokinetics: Validate ADMET predictions
- Biomarker Analysis: Confirm mechanism of action
Phase 4: Clinical Translation¶
- Phase I Trials: Safety and dosing in humans
- Phase II Trials: Efficacy in patient populations
- Phase III Trials: Large-scale validation
📊 Success Metrics & KPIs¶
Discovery Metrics¶
- Novel target identification rate: >20% improvement over classical methods
- Drug repurposing accuracy: >75% validation rate
- False positive rate: <15% for high-confidence predictions
Efficiency Metrics¶
- Time to discovery: 50% reduction in target identification time
- Cost per validated lead: 40% reduction in R&D costs
- Success rate: 30% improvement in Phase II transition
Scientific Impact¶
- Publications: High-impact journal publications
- Patents: Novel drug-target interaction discoveries
- Collaborations: Partnerships with pharmaceutical companies
🚀 Future Directions¶
1. Multi-Modal Integration¶
# Integrate genomics, proteomics, and clinical data
multi_modal_graph = EntangledGraph(hilbert_dim=16)
# Add genomic variants as quantum states
for variant in genomic_variants:
multi_modal_graph.add_quantum_node(variant.id,
state=variant.effect_vector)
# Entangle with drug responses
for drug, variant in pharmacogenomic_pairs:
multi_modal_graph.add_entangled_edge(drug, variant,
relations=["modulates_response"],
amplitudes=[response_strength])
2. Real-Time Clinical Integration¶
- Electronic Health Records: Real-time drug interaction monitoring
- Personalized Medicine: Patient-specific quantum drug profiles
- Clinical Decision Support: Quantum-enhanced treatment recommendations
3. AI-Quantum Hybrid Systems¶
- Quantum-Classical Neural Networks: Hybrid learning architectures
- Quantum Feature Learning: Automated molecular representation
- Quantum Reinforcement Learning: Optimal drug design strategies
4. Quantum Hardware Integration¶
- NISQ Devices: Near-term quantum processors for small molecules
- Quantum Simulators: Large-scale molecular system simulation
- Quantum Advantage: Exponential speedup for complex drug interactions
🔗 Integration with Existing Tools¶
Cheminformatics Integration¶
from rdkit import Chem
from qekgr.integrations import RDKitQuantumBridge
def integrate_rdkit_molecules(mol_smiles_list, graph):
"""Integrate RDKit molecules into quantum graph."""
bridge = RDKitQuantumBridge(graph)
for smiles in mol_smiles_list:
mol = Chem.MolFromSmiles(smiles)
# Convert molecular descriptors to quantum state
quantum_state = bridge.mol_to_quantum_state(mol)
# Add to graph
graph.add_quantum_node(smiles, state=quantum_state,
metadata={'rdkit_mol': mol})
Bioinformatics Integration¶
from Bio import SeqIO
from qekgr.integrations import BioPythonQuantumBridge
def integrate_protein_sequences(fasta_file, graph):
"""Integrate protein sequences as quantum states."""
bridge = BioPythonQuantumBridge(graph)
for record in SeqIO.parse(fasta_file, "fasta"):
# Convert sequence to quantum representation
quantum_state = bridge.sequence_to_quantum_state(record.seq)
graph.add_quantum_node(record.id, state=quantum_state,
metadata={'sequence': str(record.seq)})
Clinical Data Integration¶
import pandas as pd
from qekgr.integrations import ClinicalDataBridge
def integrate_clinical_data(clinical_df, graph):
"""Integrate clinical trial data into quantum graph."""
bridge = ClinicalDataBridge(graph)
for _, trial in clinical_df.iterrows():
# Convert trial outcomes to quantum states
outcome_state = bridge.outcome_to_quantum_state(trial)
graph.add_quantum_node(f"trial_{trial['id']}",
state=outcome_state,
metadata=trial.to_dict())
This comprehensive drug discovery use case demonstrates the revolutionary potential of quantum entangled knowledge graphs in pharmaceutical research. By modeling molecular interactions as quantum systems, QE-KGR enables unprecedented insights into drug mechanisms, novel target discovery, and optimized combination therapies.
The quantum approach provides significant advantages over classical methods, including natural modeling of uncertainty, non-local correlations, and quantum interference effects that classical graphs cannot capture. This leads to more accurate predictions, faster discovery timelines, and ultimately better therapeutic outcomes for patients.
Ready to revolutionize drug discovery with quantum mechanics? Let's build the future of pharmaceutical research! 🧬⚛️🚀