Six quantum simulations for predicting health outcomes from protein structures. From drug binding to DNA repair, powered by AlphaFold integration.
Each simulation exploits a different quantum phenomenon to predict specific health outcomes.
Energy Transfer Efficiency
Simulate photon-like energy transport through protein chromophores for mitochondrial health assessment.
Protein Misfolding Risk
Calculate collective quantum fluctuations to predict protein stability and aggregation propensity.
Drug Efficacy Prediction
Model transition probabilities for drug-target binding using VQE and QAOA algorithms.
Enzyme Catalysis Rate
Calculate proton tunneling rates through enzyme barriers for metabolic disease treatment.
Neural Signaling Fidelity
Analyze coherent states in microtubules for neural information processing assessment.
DNA Repair Capacity
Simulate radical pair dynamics for DNA repair mechanism and magnetoreception studies.
Fetch any of 200M+ protein structures from AlphaFold by UniProt ID, or upload your own PDB files. Our quantum simulations use the 3D structure to predict health-relevant quantum observables.
import quantumflow as qf
# Fetch EGFR from AlphaFold
structure = qf.alphafold.fetch("P00533")
print(f"pLDDT: {structure.mean_plddt:.1f}")
# Run drug binding simulation
result = qf.health.simulate(
structure,
simulation_type="AMP",
backend="ibm"
)
print(f"Binding Energy: {result.binding_energy:.1f} kcal/mol")
print(f"Efficacy Score: {result.efficacy_score:.2f}")
print(f"Binding Strength: {result.binding_strength}")
# Run all 6 simulations
all_results = qf.health.batch_simulate(
structure,
types=["WPD", "MBD", "AMP", "TUN", "COH", "ENT"]
)
for sim in all_results:
print(f"{sim.type}: {sim.health_score:.2f}")From AlphaFold structure to health prediction in a seamless quantum workflow.
Start with our free tier and access all six quantum health simulations.