💊 Pharma Innovation

Enterprise quantum computing solutions for pharmaceutical AI labs.

Why QCOS for Pharma?

Requirement	QCOS Solution
Enterprise SLA	99.9% uptime guarantee
Security	SOC2, HIPAA-ready infrastructure
Scale	100+ qubits, millions of shots
Integration	REST API, Python SDK, webhooks
Support	24/7 dedicated support

Enterprise Features

Feature	Pro	Enterprise
Daily jobs	100	Unlimited
Max qubits	50	100
Max shots	100K	10M
Priority queue	Standard	Dedicated
SLA	Best effort	99.9%
Support	Email	24/7 + Slack
SSO	❌	✅
Audit logs	❌	✅
Custom endpoints	❌	✅
On-premises	❌	Available

Use Case: Drug Discovery Pipeline

End-to-End Quantum-Enhanced Drug Discovery

from qcos import QCOSClient
from qcos.enterprise import AuditLogger, ComplianceReport
from dataclasses import dataclass
from typing import List, Dict, Optional
import json
from datetime import datetime

# Enterprise client with audit logging
client = QCOSClient(
    api_key="enterprise-key",
    organization="novartis.com",
    audit_logging=True,
    compliance_mode="pharma"
)

@dataclass
class MoleculeCandidate:
    id: str
    smiles: str
    target: str
    phase: str
    properties: Dict

@dataclass
class QuantumAnalysisResult:
    molecule_id: str
    binding_affinity: float
    stability_score: float
    toxicity_risk: float
    quantum_confidence: float
    execution_metadata: Dict

class PharmaDrugDiscoveryPipeline:
    """
    Enterprise drug discovery pipeline with quantum enhancement.
    """
    
    def __init__(self, client: QCOSClient):
        self.client = client
        self.audit = AuditLogger(client)
        self.results: List[QuantumAnalysisResult] = []
    
    def analyze_binding_affinity(self, molecule: MoleculeCandidate) -> float:
        """
        Calculate drug-target binding affinity using VQE.
        """
        # Convert molecule to quantum features
        features = self._molecule_to_features(molecule.smiles)
        
        # Create VQE circuit for binding energy
        circuit = self._create_binding_circuit(features)
        
        # Execute with high precision
        result = self.client.execute(
            qasm=circuit,
            shots=100000  # High precision for pharma
        )
        
        # Log for compliance
        self.audit.log({
            'operation': 'binding_affinity',
            'molecule_id': molecule.id,
            'target': molecule.target,
            'job_id': result.job_id
        })
        
        # Calculate binding affinity from quantum results
        affinity = self._calculate_affinity(result.counts)
        return affinity
    
    def analyze_molecular_stability(self, molecule: MoleculeCandidate) -> float:
        """
        Analyze molecular stability using quantum simulation.
        """
        features = self._molecule_to_features(molecule.smiles)
        
        # Create stability analysis circuit
        circuit = self._create_stability_circuit(features)
        
        result = self.client.execute(qasm=circuit, shots=50000)
        
        self.audit.log({
            'operation': 'stability_analysis',
            'molecule_id': molecule.id,
            'job_id': result.job_id
        })
        
        stability = self._calculate_stability(result.counts)
        return stability
    
    def predict_toxicity(self, molecule: MoleculeCandidate) -> float:
        """
        Predict toxicity risk using quantum ML model.
        """
        features = self._molecule_to_features(molecule.smiles)
        
        # Quantum classifier for toxicity
        circuit = self._create_toxicity_classifier(features)
        
        result = self.client.execute(qasm=circuit, shots=10000)
        
        self.audit.log({
            'operation': 'toxicity_prediction',
            'molecule_id': molecule.id,
            'job_id': result.job_id
        })
        
        toxicity_risk = self._calculate_toxicity_risk(result.counts)
        return toxicity_risk
    
    def full_analysis(self, molecule: MoleculeCandidate) -> QuantumAnalysisResult:
        """
        Run complete quantum analysis pipeline.
        """
        start_time = datetime.now()
        
        # Run all analyses
        binding = self.analyze_binding_affinity(molecule)
        stability = self.analyze_molecular_stability(molecule)
        toxicity = self.predict_toxicity(molecule)
        
        # Calculate confidence based on shot count and consistency
        confidence = self._calculate_confidence(binding, stability, toxicity)
        
        result = QuantumAnalysisResult(
            molecule_id=molecule.id,
            binding_affinity=binding,
            stability_score=stability,
            toxicity_risk=toxicity,
            quantum_confidence=confidence,
            execution_metadata={
                'timestamp': start_time.isoformat(),
                'duration': (datetime.now() - start_time).total_seconds(),
                'platform': 'QCOS',
                'qubits_used': 20,
                'total_shots': 160000
            }
        )
        
        self.results.append(result)
        return result
    
    def _molecule_to_features(self, smiles: str) -> List[float]:
        """Convert SMILES to quantum circuit features."""
        # Simplified - real implementation uses RDKit/OpenBabel
        import hashlib
        h = hashlib.md5(smiles.encode()).hexdigest()
        features = [int(h[i:i+2], 16) / 255 * 3.14159 for i in range(0, 16, 2)]
        return features
    
    def _create_binding_circuit(self, features: List[float]) -> str:
        """Create VQE-inspired circuit for binding calculation."""
        n = len(features)
        qasm = f"""
OPENQASM 2.0;
include "qelib1.inc";
qreg q[{n}];
creg c[{n}];

// Feature encoding (molecular properties)
"""
        for i, f in enumerate(features):
            qasm += f"ry({f}) q[{i}];\n"
            qasm += f"rz({f*0.5}) q[{i}];\n"
        
        # Entangling layer (molecular interactions)
        qasm += "\n// Interaction layer\n"
        for i in range(n - 1):
            qasm += f"cx q[{i}], q[{i+1}];\n"
            qasm += f"rz(0.5) q[{i+1}];\n"
            qasm += f"cx q[{i}], q[{i+1}];\n"
        
        # Measurement
        qasm += "\nmeasure q -> c;\n"
        return qasm
    
    def _create_stability_circuit(self, features: List[float]) -> str:
        """Create circuit for stability analysis."""
        # Similar structure with different entanglement pattern
        return self._create_binding_circuit(features)  # Simplified
    
    def _create_toxicity_classifier(self, features: List[float]) -> str:
        """Create quantum classifier for toxicity."""
        return self._create_binding_circuit(features)  # Simplified
    
    def _calculate_affinity(self, counts: Dict[str, int]) -> float:
        """Calculate binding affinity from measurement results."""
        total = sum(counts.values())
        # Higher affinity = more probability in low-energy states
        low_energy_states = sum(
            count for state, count in counts.items() 
            if state.count('1') <= 2
        )
        return low_energy_states / total
    
    def _calculate_stability(self, counts: Dict[str, int]) -> float:
        """Calculate stability score."""
        return self._calculate_affinity(counts)  # Simplified
    
    def _calculate_toxicity_risk(self, counts: Dict[str, int]) -> float:
        """Calculate toxicity risk score."""
        total = sum(counts.values())
        high_risk_states = sum(
            count for state, count in counts.items()
            if state.count('1') >= 5
        )
        return high_risk_states / total
    
    def _calculate_confidence(self, *scores) -> float:
        """Calculate confidence based on score consistency."""
        import statistics
        if len(scores) < 2:
            return 0.9
        variance = statistics.variance(scores)
        return max(0.5, min(0.99, 1 - variance))
    
    def generate_report(self) -> str:
        """Generate compliance-ready report."""
        report = f"""
# Quantum Drug Discovery Analysis Report

Generated: {datetime.now().isoformat()}
Platform: QCOS Enterprise
Compliance: GxP-ready

## Executive Summary

Analyzed {len(self.results)} molecule candidates using quantum-enhanced methods.

## Results Summary

| Molecule ID | Binding | Stability | Toxicity | Confidence |
|-------------|---------|-----------|----------|------------|
"""
        for r in self.results:
            report += f"| {r.molecule_id} | {r.binding_affinity:.3f} | "
            report += f"{r.stability_score:.3f} | {r.toxicity_risk:.3f} | "
            report += f"{r.quantum_confidence:.1%} |\n"
        
        # Top candidates
        sorted_results = sorted(
            self.results,
            key=lambda x: x.binding_affinity * (1 - x.toxicity_risk),
            reverse=True
        )
        
        report += "\n## Top Candidates\n\n"
        for i, r in enumerate(sorted_results[:5]):
            score = r.binding_affinity * (1 - r.toxicity_risk)
            report += f"{i+1}. **{r.molecule_id}** - Score: {score:.4f}\n"
        
        report += """
## Methodology

All calculations performed using QCOS GPU-accelerated quantum simulation:
- Backend: cuStateVec on NVIDIA A100
- Max qubits: 20
- Shots per calculation: 10,000-100,000
- Confidence intervals: 95%

## Compliance Notes

- Full audit trail available
- All data encrypted at rest (AES-256)
- Processing in EU jurisdiction (LUMI, Finland)
"""
        return report

# Usage example
pipeline = PharmaDrugDiscoveryPipeline(client)

# Analyze candidates
candidates = [
    MoleculeCandidate(
        id='NVS-2024-001',
        smiles='CC(=O)OC1=CC=CC=C1C(=O)O',  # Aspirin
        target='COX-2',
        phase='Discovery',
        properties={'mw': 180.16}
    ),
    MoleculeCandidate(
        id='NVS-2024-002',
        smiles='CN1C=NC2=C1C(=O)N(C(=O)N2C)C',  # Caffeine
        target='A2A',
        phase='Discovery',
        properties={'mw': 194.19}
    ),
]

print("Analyzing candidates...")
for candidate in candidates:
    result = pipeline.full_analysis(candidate)
    print(f"\n{candidate.id}:")
    print(f"  Binding: {result.binding_affinity:.3f}")
    print(f"  Stability: {result.stability_score:.3f}")
    print(f"  Toxicity: {result.toxicity_risk:.3f}")
    print(f"  Confidence: {result.quantum_confidence:.1%}")

# Generate report
report = pipeline.generate_report()
with open('drug_discovery_report.md', 'w') as f:
    f.write(report)
print("\n✓ Report generated: drug_discovery_report.md")

Use Case: High-Throughput Screening

from qcos import AsyncQCOSClient
import asyncio
from typing import List
import time

async def high_throughput_screening(
    molecules: List[dict],
    batch_size: int = 100
) -> List[dict]:
    """
    Screen thousands of molecules efficiently using async execution.
    """
    async with AsyncQCOSClient() as client:
        results = []
        start_time = time.time()
        
        # Process in batches
        for batch_start in range(0, len(molecules), batch_size):
            batch = molecules[batch_start:batch_start + batch_size]
            
            # Submit all in batch concurrently
            tasks = []
            for mol in batch:
                circuit = create_screening_circuit(mol['features'])
                task = client.submit(qasm=circuit, shots=1000)
                tasks.append((mol['id'], task))
            
            # Wait for batch results
            batch_results = []
            for mol_id, task in tasks:
                job = await task
                result = await job.wait(timeout=60)
                
                score = calculate_score(result.counts)
                batch_results.append({
                    'molecule_id': mol_id,
                    'score': score,
                    'job_id': result.job_id
                })
            
            results.extend(batch_results)
            
            # Progress
            processed = min(batch_start + batch_size, len(molecules))
            elapsed = time.time() - start_time
            rate = processed / elapsed
            print(f"Progress: {processed}/{len(molecules)} ({rate:.1f} mol/s)")
        
        return sorted(results, key=lambda x: x['score'], reverse=True)

# Screen 10,000 molecules
molecules = load_molecule_library(n=10000)

print("Starting high-throughput screening...")
top_hits = asyncio.run(high_throughput_screening(molecules))

print(f"\nTop 10 hits:")
for hit in top_hits[:10]:
    print(f"  {hit['molecule_id']}: {hit['score']:.4f}")

Enterprise Integration

SSO Integration (SAML/OIDC)

# sso-config.yaml
authentication:
  provider: azure-ad  # or okta, onelogin, etc.
  client_id: ${AZURE_CLIENT_ID}
  tenant_id: ${AZURE_TENANT_ID}
  scopes:
    - openid
    - profile
    - email
  role_mapping:
    Admin: enterprise_admin
    Researcher: enterprise_user
    Viewer: read_only

Webhook Notifications

# Configure webhooks for job completion
client.configure_webhooks({
    'job_completed': 'https://internal.novartis.com/qcos/webhook',
    'job_failed': 'https://internal.novartis.com/qcos/alert',
    'quota_warning': 'https://internal.novartis.com/qcos/quota'
})

Audit Log Export

# Export audit logs for compliance
audit_logs = client.export_audit_logs(
    start_date='2024-01-01',
    end_date='2024-12-31',
    format='json'
)

# Save for compliance review
with open('qcos_audit_2024.json', 'w') as f:
    json.dump(audit_logs, f, indent=2)

Compliance & Security

GxP Readiness

QCOS enterprise tier provides:

21 CFR Part 11 compatible audit trails
EU Annex 11 data integrity
GAMP 5 validated infrastructure

Data Security

Control	Implementation
Encryption (transit)	TLS 1.3
Encryption (rest)	AES-256
Access control	RBAC + SSO
Key management	Azure Key Vault
Network	Private endpoints available

Dedicated Infrastructure

Enterprise customers can request:

Dedicated GPU allocation - Reserved capacity
Private endpoints - VPN/ExpressRoute integration
Data residency - Specific region deployment
On-premises - Air-gapped installation

Pricing

Enterprise Tier

Component	Price
Base platform	Custom
Per 1M shots	Negotiated
Dedicated GPUs	$X/hour
Support (24/7)	Included
Training	Included

Contact us for a custom quote tailored to your organization's needs.

Case Studies

Global Pharma (Fortune 500)

"QCOS reduced our computational screening time from weeks to hours, enabling us to evaluate 10x more candidates in our early discovery pipeline."

— Director of Computational Chemistry

Biotech Startup

"The enterprise SLA and audit logging made it easy to integrate QCOS into our GxP-compliant workflows."

— Head of Data Science

Contact

For enterprise sales and demos:

📧 enterprise@softquantus.com

📞 +1 (555) QUANTUM

We offer:

Custom proof-of-concept
On-site technical workshop
Integration support
Dedicated account team

Why QCOS for Pharma?​

Enterprise Features​

Use Case: Drug Discovery Pipeline​

End-to-End Quantum-Enhanced Drug Discovery​

Use Case: High-Throughput Screening​

Enterprise Integration​

SSO Integration (SAML/OIDC)​

Webhook Notifications​

Audit Log Export​

Compliance & Security​

GxP Readiness​

Data Security​

Dedicated Infrastructure​

Pricing​

Enterprise Tier​

Case Studies​

Global Pharma (Fortune 500)​

Biotech Startup​

Contact​