API Reference

quantum(program, backend, api_key, shots)

Main entry point for quantum computations with natural language interface.

from bioql import quantum

result = quantum(
    program="dock aspirin to COX-1",
    backend='simulator',
    api_key='your_key',
    shots=4096
)

run_complete_pipeline(molecule_smiles, target_protein_pdb)

Execute complete drug discovery pipeline: docking, affinity, ADME, toxicity.

from bioql import run_complete_pipeline

results = run_complete_pipeline(
    molecule_smiles="CC(=O)OC1=CC=CC=C1C(=O)O",
    target_protein_pdb="cox1.pdb",
    backend='ibm_quantum'
)

MolecularDockingCircuit

Quantum molecular docking with VQE algorithm.

Methods: run_docking(), get_poses(), calculate_affinity()

ADMECircuit

Quantum ML for pharmacokinetics prediction.

Methods: batch_predict(), predict_absorption(), calculate_bioavailability()

ToxicityPredictionCircuit

Multi-endpoint toxicity screening with quantum classifiers.

Methods: predict_toxicity(), get_alerts(), calculate_risk_score()

analyze_protein_sequence(sequence, analysis_type, shots)

Analyze protein sequences for physicochemical properties, secondary structure, and functional domains.

from bioql.omics import ProteomicsCircuit

circuit = ProteomicsCircuit(backend='ibm_quantum')
result = circuit.analyze_protein_sequence(
    sequence="MKLLVLCLAAALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGK",
    analysis_type="comprehensive",
    shots=4096
)

print(result.molecular_weight)
print(result.isoelectric_point)
print(result.secondary_structure)

predict_ptm_sites(sequence, modification_types, confidence_threshold)

Predict post-translational modification sites including phosphorylation, acetylation, and methylation.

from bioql.omics import ProteomicsCircuit

circuit = ProteomicsCircuit(backend='ionq')
ptm_result = circuit.predict_ptm_sites(
    sequence="MKLLVLCLAAALARPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGK",
    modification_types=['phosphorylation', 'acetylation'],
    confidence_threshold=0.75
)

for site in ptm_result.predicted_sites:
    print(f"Position {site.position}: {site.modification_type}")
    print(f"Confidence: {site.confidence}")

identify_metabolite(mass_spectrum, ionization_mode, database)

Identify metabolites from mass spectrometry data using quantum similarity search.

from bioql.omics import MetabolomicsCircuit

circuit = MetabolomicsCircuit(backend='ibm_quantum')
result = circuit.identify_metabolite(
    mass_spectrum={
        'peaks': [180.063, 342.116, 504.169],
        'intensities': [100, 85, 45]
    },
    ionization_mode='positive',
    database='hmdb',
    shots=4096
)

print(f"Match: {result.top_match.name}")
print(f"Score: {result.top_match.score}")

analyze_metabolic_pathway(pathway_id, metabolite_concentrations, conditions)

Analyze metabolic pathways and predict flux distributions using QAOA optimization.

from bioql.omics import MetabolomicsCircuit

circuit = MetabolomicsCircuit(backend='simulator')
pathway_result = circuit.analyze_metabolic_pathway(
    pathway_id='glycolysis',
    metabolite_concentrations={
        'glucose': 5.0,
        'pyruvate': 0.8,
        'lactate': 1.2
    },
    conditions={'pH': 7.4, 'temperature': 37}
)

print(f"Pathway Flux: {pathway_result.total_flux}")
print(f"Bottleneck: {pathway_result.bottleneck_reaction}")

integrate_omics_layers(genomics_data, transcriptomics_data, proteomics_data, metabolomics_data)

Integrate multiple omics layers for cross-layer correlation and pathway enrichment analysis.

from bioql.omics import MultiOmicsIntegrator

integrator = MultiOmicsIntegrator(backend='ibm_quantum')
result = integrator.integrate_omics_layers(
    genomics_data={'variants': ['rs123456', 'rs789012']},
    transcriptomics_data={'gene_expression': {'EGFR': 8.5}},
    proteomics_data={'protein_abundance': {'EGFR': 12.3}},
    metabolomics_data={'metabolites': {'glucose': 5.0}},
    integration_method='quantum_correlation',
    shots=8192
)

print(f"Integration Score: {result.integration_score}")
for corr in result.correlations:
    print(f"{corr.layer1} - {corr.layer2}: r={corr.coefficient}")

call_variants(bam_file, reference_genome, min_quality, min_depth)

Call genetic variants from sequencing data with quantum-enhanced accuracy.

from bioql.omics import GenomicsCircuit

circuit = GenomicsCircuit(backend='ibm_quantum')
variants = circuit.call_variants(
    bam_file='sample_aligned.bam',
    reference_genome='hg38',
    min_quality=30,
    min_depth=10,
    shots=4096
)

print(f"Total Variants: {len(variants.variants)}")
print(f"SNPs: {variants.num_snps}")
print(f"Indels: {variants.num_indels}")

analyze_rna_seq(counts_matrix, sample_groups, normalization, fdr_threshold)

Perform differential gene expression analysis using quantum machine learning.

from bioql.omics import GenomicsCircuit

circuit = GenomicsCircuit(backend='ionq')
rna_result = circuit.analyze_rna_seq(
    counts_matrix='gene_counts.csv',
    sample_groups={
        'control': ['S1', 'S2', 'S3'],
        'treatment': ['S4', 'S5', 'S6']
    },
    normalization='tmm',
    fdr_threshold=0.05
)

print(f"DEGs: {len(rna_result.deg_list)}")
print(f"Upregulated: {rna_result.num_upregulated}")
print(f"Downregulated: {rna_result.num_downregulated}")

Backend Usage Example

from bioql import quantum

# Run on IBM Quantum
result = quantum(
    program="dock aspirin to COX-1",
    backend='ibm_quantum',
    api_key='your_ibm_key',
    shots=4096
)

# Run on IonQ
result = quantum(
    program="dock aspirin to COX-1",
    backend='ionq',
    api_key='your_ionq_key',
    shots=4096
)

# Or use the fast simulator for development
result = quantum(
    program="dock aspirin to COX-1",
    backend='simulator',
    shots=1024
)