Enterprise-grade quantum platform for molecular docking, binding affinity prediction, ADME/Tox analysis, and CRISPR gene editing optimization. Natural language interface powered by real quantum hardware.
from bioql import quantum
# Natural language quantum programming
result = quantum(
"dock aspirin to COX-1 and predict binding affinity",
backend='ibm_quantum',
api_key='your_api_key',
shots=4096
)
# Results with quantum advantage
print(f"Binding Energy: {result.binding_energy} kcal/mol")
print(f"Affinity Kd: {result.affinity_kd} nM")
print(f"Drug-likeness: {result.lipinski_compliant}")Install BioQL via PyPI and start running quantum computations
pip install bioqlimport bioql
print(bioql.__version__)
# 7.0.3Latest version: 7.0.3 β’ Python 3.8+ β’ View on PyPI β
The most complete quantum drug discovery platform with proven results
Write drug discovery workflows in plain English. No quantum gates knowledge required. 164B+ NLP patterns.
Run on IBM Torino (133 qubits), IonQ trapped ions, Google Cirq, Azure Quantum, and AWS Braket.
7 production-ready modules: Docking, Binding Affinity, ADME, Toxicity, Pharmacophore, Protein Folding, De Novo Design.
Quantum-enhanced CRISPR guide RNA optimization with off-target prediction and phenotype inference.
21 CFR Part 11 aligned provenance logging, cryptographic audit trails, and full reproducibility tracking.
Smart caching (24x speedup), job batching (30% cost savings), circuit optimization (35% gate reduction).
Production-ready quantum algorithms for every stage of drug development
Predict ligand-receptor binding poses with quantum advantage
from bioql.circuits import MolecularDockingCircuit
docking = MolecularDockingCircuit(
ligand_smiles='CC(=O)OC1=CC=CC=C1C(=O)O',
receptor_pdb='cox1.pdb',
num_poses=20
)
result = docking.run_docking(shots=4096)
print(result.best_energy) # -8.5 kcal/molVQE-based quantum chemistry for precise Kd/Ki calculation
from bioql.circuits import BindingAffinityCircuit
circuit = BindingAffinityCircuit(
ligand_smiles="CC(=O)OC1=CC=CC=C1C(=O)O",
receptor_pdb="protein.pdb",
n_qubits=12,
vqe_depth=3
)
result = circuit.estimate_affinity()
print(result.binding_affinity_kd) # 1.45 nMQuantum ML for pharmacokinetics: Absorption, Distribution, Metabolism, Excretion
from bioql.circuits import ADMECircuit
circuit = ADMECircuit(
molecule_smiles="CC(=O)OC1=CC=CC=C1C(=O)O"
)
result = circuit.batch_predict()
print(result.bioavailability) # 65.3%
print(result.half_life) # 4.2 hoursMulti-endpoint toxicity screening with quantum classifiers
from bioql.circuits import ToxicityPredictionCircuit
circuit = ToxicityPredictionCircuit(
molecule_smiles="c1ccccc1N(=O)=O"
)
result = circuit.predict_toxicity()
print(result.overall_risk) # 0.65 (high)
print(result.alerts) # ['nitro_aromatic']3D feature extraction for virtual screening and lead optimization
from bioql.circuits import PharmacophoreCircuit
circuit = PharmacophoreCircuit(
molecule_smiles="CC(=O)OC1=CC=CC=C1C(=O)O",
n_conformers=20
)
model = circuit.generate_pharmacophore()
print(model.features) # H-bond donors/acceptors...QAOA-based optimization for tertiary structure prediction
from bioql.circuits import ProteinFoldingCircuit
folding = ProteinFoldingCircuit()
circuit = folding.build(
sequence_length=10,
lattice_dimensions=2,
qaoa_layers=3
)
result = folding.optimize()
print(result.folding_energy)Quantum-enhanced generation of novel drug-like molecules from scratch
from bioql import quantum
# Design new drug for specific target
result = quantum(
"design new GLP-1 agonist with high bioavailability",
backend='ibm_quantum',
api_key='your_key',
shots=4096
)
print(result.designed_molecules) # Novel drug candidates
print(result.best_molecule.smiles) # Top candidate
Run on the world's most advanced quantum computers
Pay only for the quantum shots you execute. No subscriptions, no hidden fees.
All billing managed through Stripe. Usage tracked in real-time. Pay as you go.
Choose your backend and pay per shot executed
| Backend | Qubits | Price/Shot | 1000 Shots |
|---|---|---|---|
| π₯οΈ Simulators | |||
| IonQ Ideal Simulator | 29q | $0.01 | $10 |
| AWS SV1 Simulator | 34q | $0.01 | $10 |
| AWS TN1 Simulator | 50q | $0.02 | $20 |
| βοΈ Quantum Hardware | |||
| IBM Torino | 133q | $3.00 | $3,000 |
| IBM Brisbane | 127q | $3.00 | $3,000 |
| IonQ Forte | 36q | $3.00 | $3,000 |
| IonQ QPU | 36q | $2.00 | $2,000 |
| QuEra Aquila | 256q | $5.00 | $5,000 |
You're charged per shot executed based on the quantum backend you choose. Simulators cost $0.01-0.02 per shot, while quantum hardware ranges from $2-5 per shot. All billing is handled securely through Stripe with real-time usage tracking. No minimum commitment required.
Free tier: 100 shots/month β’ No credit card required for testing
A collaborative, Jupyter-style notebook for quantum biology research with real-time execution, AI assistance, and automatic paper generation
BioQL Lab Notebook is a modern, web-based computational notebook designed specifically for quantum biology and drug discovery workflows. It combines the familiar notebook interface with powerful quantum computing capabilities, real-time collaboration, and AI-powered assistance.
Create notebooks with Code cells (Python/BioQL), Markdown cells for documentation, and dedicated @BioQL cells for quantum queries. Mix and match to create comprehensive research narratives.
Execute BioQL commands directly in your browser and see results instantly. Connect to quantum backends, run molecular docking simulations, and analyze results without leaving your notebook.
Work together with your team in real-time. See cursor positions, live edits, and share insights instantly via WebSocket connections. Perfect for distributed research teams.
Built-in AI assistant helps write BioQL queries, suggests next experimental steps, finds relevant papers, and explains complex quantum concepts. Like having a computational biology expert at your fingertips.
Click "New Notebook" or select an existing notebook from the sidebar. Choose from pre-built templates for protein analysis, drug discovery, or multi-omics workflows.
Add cells with the "+" button. Choose between Code (Python), Markdown (documentation), or @BioQL (quantum queries). Use Monaco Editor with syntax highlighting and autocomplete.
Write BioQL queries like @bioql dock aspirin to COX-2 and run cells with βΆοΈ. Results appear instantly with formatted JSON output.
View 3D molecular structures in the built-in viewer. Export your entire notebook as a scientific paper (Markdown, LaTeX, or PDF) with one click.
Invite collaborators via the Share button. They'll see your cursor, edits in real-time, and can contribute simultaneously. Version history tracks all changes.
Document your entire protein analysis pipeline: fetch sequences from PDB, predict structure with quantum algorithms, analyze binding sites, and generate publication-ready figures.
Run complete drug discovery pipelines: molecular docking, binding affinity calculations, ADME/Tox predictions, and hit-to-lead optimization. Track everything in one notebook.
Combine genomics, proteomics, and metabolomics data. Run quantum correlation analysis, pathway enrichment, and generate comprehensive multi-omics reports.
Design guide RNAs, predict off-target effects with quantum algorithms, optimize delivery vectors, and document your entire gene editing strategy.
Revolutionary quantum-enhanced scientific visualization with automatic parameter optimization. No quantum expertise required.
Quantum Graphs is an intelligent visualization engine that uses quantum computing to generate high-fidelity computational images for biotechnology research. The revolutionary Adaptive Quantum Optimizer automatically determines the optimal configurationβqubits, shots, QEC codes, and backendβwithout requiring any quantum expertise.
You provide molecular data. Quantum Graphs automatically calculates:
3D visualization, secondary structure analysis, folding pathways, conformational sampling with quantum algorithms (QAOA)
Molecular docking, binding affinity prediction, ADME visualization, drug-target interaction maps
Metabolic pathway networks, flux balance analysis, concentration heatmaps, biomarker discovery
Electron density maps, molecular orbitals, energy landscapes with VQE, reaction pathways
Antibody-antigen binding visualization, CDR analysis, affinity optimization
Free Tier: 10 visualizations/month (simulator, up to 100 atoms, 1000 shots)
Paid Plans: Pay-as-you-go based on:
Cost Estimate Before Execution - You'll know the exact price before running any job!
10,000 atoms
24 qubits β’ 5,000 shots
IonQ Forte β’ $15,000
10 metabolites
12 qubits β’ 3,000 shots
IBM Torino β’ $9,000
30 atoms
8 qubits β’ 1,000 shots
IonQ Simulator β’ $10
200 residues
18 qubits β’ 4,000 shots
IonQ QPU β’ $8,000
Design complex quantum drug discovery pipelines with drag-and-drop simplicity
A powerful visual interface for creating multi-step quantum computing workflows. Connect nodes, configure parameters, and execute complex drug discovery pipelines without writing a single line of code.
Drag-and-drop interface with real-time connection preview and smart node placement
Background job processing with real-time queue monitoring and progress tracking
Molecular docking, ADME prediction, protein folding, drug discovery, and custom code execution
Save experiment results and chain multiple analyses for comprehensive drug discovery
Execute on IBM Quantum, IonQ, AWS Braket, and local simulators from one interface
Structured results with visualization data, quantum counts, and exportable formats
No installation required. Run quantum drug discovery in your browser.
{
"binding_energy": -8.5,
"affinity_kd": 1.45,
"best_pose": {
"rotation": [0.12, 0.45, 0.89],
"translation": [2.3, -1.2, 0.5]
},
"lipinski_compliant": true,
"quantum_advantage": "2.3x speedup vs classical"
}Use our sandbox API key to run unlimited simulations on the BioQL quantum simulator. Perfect for prototyping before moving to live hardware.
This key only works with the simulator backend and never consumes your paid balance.
bioql_fa389901656bb996c17117b168495985export BIOQL_API_KEY="bioql_fa389901656bb996c17117b168495985"
"backend": "simulator"
curl https://api.bioql.bio/api/quantum/execute -H "Content-Type: application/json" -d '{
"program": "dock aspirin to COX-1",
"api_key": "bioql_fa389901656bb996c17117b168495985",
"backend": "simulator",
"shots": 1024
}'β Responses include counts, binding estimates, and BioQL annotations. Switch to a paid plan when you're ready for real hardware.
Stay updated with the latest news, tutorials, and insights on quantum computing for drug discovery