🧬 Drug Discovery & Biotech

Accelerate molecular simulation and drug discovery with GPU-powered quantum computing.

Why QCOS for Biotech?

Challenge	QCOS Solution
Slow molecular simulations	GPU acceleration (100x faster)
Limited qubit count	Up to 100 qubits for larger molecules
Integration complexity	Native Qiskit/OpenQASM support
Reproducibility	Deterministic results + audit logging

Use Case: Molecular Ground State Energy

Calculate the ground state energy of molecules using VQE (Variational Quantum Eigensolver).

Example: H₂ Molecule

from qcos import QCOSClient
from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
import numpy as np

# Initialize QCOS client
client = QCOSClient()

def create_h2_ansatz(theta: float) -> str:
    """
    Create a simple ansatz for H2 molecule.
    """
    return f"""
    OPENQASM 2.0;
    include "qelib1.inc";
    qreg q[4];
    creg c[4];
    
    // Initial state preparation
    x q[0];
    x q[2];
    
    // Variational form (UCCSD-inspired)
    ry({theta}) q[1];
    cx q[1], q[0];
    cx q[1], q[2];
    cx q[1], q[3];
    
    // Measure in computational basis
    measure q -> c;
    """

def vqe_optimization():
    """
    Simple VQE loop to find ground state.
    """
    best_energy = float('inf')
    best_theta = 0
    
    # Parameter sweep
    for theta in np.linspace(-np.pi, np.pi, 50):
        circuit = create_h2_ansatz(theta)
        
        # Execute on QCOS GPU
        result = client.execute(qasm=circuit, shots=4096)
        
        # Calculate expectation value
        energy = calculate_energy(result.counts)
        
        if energy < best_energy:
            best_energy = energy
            best_theta = theta
            
        print(f"θ = {theta:.3f}, E = {energy:.6f} Ha")
    
    return best_theta, best_energy

def calculate_energy(counts: dict) -> float:
    """
    Calculate energy from measurement counts.
    (Simplified - real implementation uses Hamiltonian)
    """
    total = sum(counts.values())
    expectation = 0
    
    for state, count in counts.items():
        # Apply Hamiltonian weights
        parity = sum(int(b) for b in state) % 2
        expectation += (1 if parity == 0 else -1) * count
        
    return -0.5 * (expectation / total) - 1.0

# Run optimization
optimal_theta, ground_energy = vqe_optimization()
print(f"\nOptimal θ: {optimal_theta:.4f}")
print(f"Ground state energy: {ground_energy:.6f} Ha")
print(f"Literature value: -1.137 Ha")

Use Case: Protein Folding Simulation

Quantum-inspired optimization for protein structure prediction.

from qcos import QCOSClient
import itertools

client = QCOSClient()

def create_qaoa_circuit(num_qubits: int, gamma: float, beta: float) -> str:
    """
    Create QAOA circuit for protein folding optimization.
    """
    qasm = f"""
    OPENQASM 2.0;
    include "qelib1.inc";
    qreg q[{num_qubits}];
    creg c[{num_qubits}];
    
    // Initial superposition
    """
    for i in range(num_qubits):
        qasm += f"h q[{i}];\n"
    
    # Cost layer (problem-specific)
    qasm += f"\n// Cost layer (γ = {gamma})\n"
    for i in range(num_qubits - 1):
        qasm += f"cx q[{i}], q[{i+1}];\n"
        qasm += f"rz({gamma}) q[{i+1}];\n"
        qasm += f"cx q[{i}], q[{i+1}];\n"
    
    # Mixer layer
    qasm += f"\n// Mixer layer (β = {beta})\n"
    for i in range(num_qubits):
        qasm += f"rx({2*beta}) q[{i}];\n"
    
    # Measurement
    qasm += "\nmeasure q -> c;\n"
    
    return qasm

def optimize_protein_structure():
    """
    QAOA optimization for protein folding.
    """
    num_qubits = 12  # Represents amino acid configurations
    num_layers = 3
    
    best_config = None
    best_energy = float('inf')
    
    # Grid search over parameters
    for gamma in np.linspace(0, np.pi, 10):
        for beta in np.linspace(0, np.pi, 10):
            circuit = create_qaoa_circuit(num_qubits, gamma, beta)
            
            result = client.execute(qasm=circuit, shots=2048)
            
            # Find most likely configuration
            best_state = max(result.counts, key=result.counts.get)
            energy = evaluate_protein_energy(best_state)
            
            if energy < best_energy:
                best_energy = energy
                best_config = best_state
                print(f"New best: {best_state} (E = {energy:.4f})")
    
    return best_config, best_energy

# Run optimization
config, energy = optimize_protein_structure()
print(f"\nOptimal configuration: {config}")
print(f"Energy: {energy:.4f}")

Use Case: Drug-Target Binding Affinity

Quantum machine learning for predicting drug binding.

from qcos import QCOSClient
import numpy as np

client = QCOSClient()

def create_qml_circuit(features: list, weights: list) -> str:
    """
    Quantum variational classifier for drug binding.
    """
    num_qubits = len(features)
    
    qasm = f"""
    OPENQASM 2.0;
    include "qelib1.inc";
    qreg q[{num_qubits}];
    creg c[1];
    
    // Feature encoding
    """
    for i, f in enumerate(features):
        qasm += f"ry({f}) q[{i}];\n"
        qasm += f"rz({f}) q[{i}];\n"
    
    # Entangling layer
    qasm += "\n// Entangling\n"
    for i in range(num_qubits - 1):
        qasm += f"cx q[{i}], q[{i+1}];\n"
    
    # Variational layer
    qasm += "\n// Variational weights\n"
    for i, w in enumerate(weights):
        qasm += f"ry({w}) q[{i % num_qubits}];\n"
    
    # Measure first qubit for classification
    qasm += "\nmeasure q[0] -> c[0];\n"
    
    return qasm

def predict_binding(molecule_features: list, model_weights: list) -> float:
    """
    Predict binding affinity for a molecule.
    """
    circuit = create_qml_circuit(molecule_features, model_weights)
    result = client.execute(qasm=circuit, shots=1024)
    
    # Calculate binding probability
    ones = result.counts.get('1', 0)
    zeros = result.counts.get('0', 0)
    binding_probability = ones / (ones + zeros)
    
    return binding_probability

# Example: Predict binding for candidate drug
molecule = [0.5, 1.2, 0.3, 0.8, 0.6]  # Molecular descriptors
weights = [0.1, -0.3, 0.7, 0.2, -0.5, 0.4, 0.8, -0.1, 0.3, 0.6]  # Trained weights

binding = predict_binding(molecule, weights)
print(f"Binding affinity: {binding:.2%}")
print(f"Recommendation: {'High potential' if binding > 0.7 else 'Low potential'}")

Batch Processing for High-Throughput Screening

Screen thousands of molecules efficiently:

from qcos import QCOSClient
import asyncio

async def screen_molecules(molecules: list[dict]) -> list[dict]:
    """
    High-throughput screening of molecule candidates.
    """
    async with AsyncQCOSClient() as client:
        # Submit all molecules in parallel
        jobs = []
        for mol in molecules:
            circuit = create_screening_circuit(mol['features'])
            job = await client.submit(qasm=circuit, shots=1024)
            jobs.append({
                'molecule_id': mol['id'],
                'job': job
            })
        
        # Collect results
        results = []
        for item in jobs:
            result = await item['job'].wait()
            score = calculate_score(result.counts)
            results.append({
                'molecule_id': item['molecule_id'],
                'score': score,
                'counts': result.counts
            })
        
        return sorted(results, key=lambda x: x['score'], reverse=True)

# Screen 1000 molecules
molecules = load_molecule_library()  # Your molecule database
top_candidates = asyncio.run(screen_molecules(molecules[:1000]))

print("Top 10 candidates:")
for mol in top_candidates[:10]:
    print(f"  {mol['molecule_id']}: {mol['score']:.4f}")

Integration with Existing Tools

With RDKit

from rdkit import Chem
from rdkit.Chem import AllChem
from qcos import QCOSClient

def molecule_to_quantum_features(smiles: str) -> list[float]:
    """
    Convert molecule to quantum circuit features.
    """
    mol = Chem.MolFromSmiles(smiles)
    
    # Generate molecular descriptors
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=8)
    features = [float(b) * np.pi for b in fp.ToBitString()]
    
    return features

# Example
smiles = "CC(=O)OC1=CC=CC=C1C(=O)O"  # Aspirin
features = molecule_to_quantum_features(smiles)

client = QCOSClient()
circuit = create_qml_circuit(features, trained_weights)
result = client.execute(qasm=circuit, shots=2048)

With PySCF

from pyscf import gto, scf
from qcos import QCOSClient

# Define molecule in PySCF
mol = gto.Mole()
mol.atom = 'H 0 0 0; H 0 0 0.74'
mol.basis = 'sto-3g'
mol.build()

# Get Hamiltonian
mf = scf.RHF(mol)
mf.kernel()

# Convert to quantum circuit and run on QCOS
# ... (use OpenFermion or Qiskit Nature)

Performance Benchmarks

Task	Qubits	CPU Time	QCOS GPU Time	Speedup
H₂ VQE	4	2.3s	0.05s	46x
H₂O VQE	8	45s	0.8s	56x
Protein QAOA	16	5m	3.2s	94x
Drug screening (100)	8	2h	1.5m	80x

Getting Started

Sign up for a Pro or Enterprise account
Install SDK: pip install qcos-sdk
Get API key from dashboard
Start simulating!

# Quick test
pip install qcos-sdk
export QCOS_API_KEY="your-key"
python -c "from qcos import QCOSClient; print(QCOSClient().health())"

Resources

Qiskit Nature - Quantum chemistry tools
OpenFermion - Molecular simulation
PennyLane - Quantum machine learning

Contact

Ready to accelerate your drug discovery pipeline?

📧 biotech@softquantus.com

Request a demo tailored to your research needs.

Why QCOS for Biotech?​

Use Case: Molecular Ground State Energy​

Example: H₂ Molecule​

Use Case: Protein Folding Simulation​

Use Case: Drug-Target Binding Affinity​

Batch Processing for High-Throughput Screening​

Integration with Existing Tools​

With RDKit​

With PySCF​

Performance Benchmarks​

Getting Started​

Resources​

Contact​