Standalone TypeScript/JavaScript client for Quantum-Inspired RNA Folding using WebAssembly (Pyodide). No server required!
npm install rnafold-quantum-wasmbash
npm install rnafold-quantum-wasm
`
Quick Start
`typescript
import { RnaFoldQuantumClient } from 'rnafold-quantum-wasm';
async function main() {
// Create client (WebAssembly mode by default)
const client = new RnaFoldQuantumClient();
// Initialize (loads Pyodide and Python modules)
await client.initialize();
// Run the quantum RNA folding pipeline
const result = await client.runPipeline({
targetName: 'SARS-CoV-2 Spike Protein',
disease: 'COVID-19',
sequenceLength: 50,
numCandidates: 20
});
console.log(Found ${result.num_leads} lead candidates!);
console.log('Top lead:', result.leads[0]);
}
main();
`
API Reference
$3
`typescript
new RnaFoldQuantumClient(options?: { useWasm?: boolean; baseUrl?: string })
`
Options:
- useWasm (default: true): Use WebAssembly for local calculations
- baseUrl: Server URL (only needed if useWasm is false)
$3
#### initialize(): Promise
Initialize the client. Must be called before other methods when using WASM mode.
`typescript
await client.initialize();
`
#### runPipeline(options): Promise
Run the full quantum RNA folding pipeline.
Parameters:
`typescript
{
targetName?: string, // Target protein name (default: 'unknown')
disease?: string, // Disease indication (default: 'unknown')
sequenceLength?: number, // RNA sequence length (default: 50)
numCandidates?: number // Number of candidates (default: 20)
}
`
Returns:
`typescript
{
success: boolean,
target_name: string,
disease: string,
num_leads: number,
leads: Array<{
id: string,
sequence: string,
structure: string, // Dot-bracket notation
mfe: number, // Minimum free energy (kcal/mol)
stability_score: number,
base_pairs: number,
generation_method: string
}>,
metrics: {
total_candidates: number,
sequence_length: number,
avg_stability: number,
avg_mfe: number
}
}
`
Example:
`typescript
const result = await client.runPipeline({
targetName: 'HIV-1 TAR',
disease: 'HIV',
sequenceLength: 60,
numCandidates: 30
});
console.log(Generated ${result.num_leads} leads);
console.log(Average MFE: ${result.metrics.avg_mfe} kcal/mol);
`
#### predictStructure(sequence): Promise
Predict secondary structure for a single RNA sequence.
Parameters:
- sequence: RNA sequence string (A, U, G, C)
Returns:
`typescript
{
sequence: string,
structure: string, // Dot-bracket notation
mfe: number, // Minimum free energy
base_pairs: number // Number of base pairs
}
`
Example:
`typescript
const result = await client.predictStructure('AUCGAUCGAUCG');
console.log('Structure:', result.structure); // e.g., "((.......))"
console.log('MFE:', result.mfe, 'kcal/mol');
`
#### generateCandidates(length, numCandidates): Promise
Generate RNA candidates using quantum-inspired sampling.
Parameters:
- length: Length of RNA sequences to generate
- numCandidates: Number of candidates to generate
Example:
`typescript
const candidates = await client.generateCandidates(40, 15);
candidates.forEach(c => {
console.log(${c.id}: ${c.sequence});
console.log( Structure: ${c.structure});
console.log( Stability: ${c.stability_score});
});
`
Examples
$3
`typescript
import { RnaFoldQuantumClient } from 'rnafold-quantum-wasm';
const client = new RnaFoldQuantumClient();
await client.initialize();
const result = await client.predictStructure('GGGAAACCC');
console.log(result);
// {
// sequence: 'GGGAAACCC',
// structure: '(((...)))',
// mfe: -4.2,
// base_pairs: 3
// }
`
$3
`typescript
const candidates = await client.generateCandidates(30, 10);
// Sort by stability
candidates.sort((a, b) => b.stability_score - a.stability_score);
console.log('Most stable candidate:');
console.log('Sequence:', candidates[0].sequence);
console.log('Structure:', candidates[0].structure);
console.log('MFE:', candidates[0].mfe, 'kcal/mol');
`
$3
`typescript
const result = await client.runPipeline({
targetName: 'KRAS G12C',
disease: 'Lung Cancer',
sequenceLength: 55,
numCandidates: 50
});
// Export top leads
const topLeads = result.leads.slice(0, 5);
console.log('Top 5 Lead Candidates:');
topLeads.forEach((lead, i) => {
console.log(\n${i + 1}. ${lead.id});
console.log( Sequence: ${lead.sequence});
console.log( Structure: ${lead.structure});
console.log( MFE: ${lead.mfe} kcal/mol);
console.log( Stability: ${lead.stability_score.toFixed(2)});
console.log( Method: ${lead.generation_method});
});
`
How It Works
This package uses Pyodide to run Python RNA folding algorithms directly in JavaScript/TypeScript:
1. Pyodide Loading: On first use, Pyodide (Python compiled to WebAssembly) is loaded
2. Python Module: A simplified RNA folding module is loaded into the Python environment
3. Algorithm Execution: Algorithms run in WASM for near-native performance
4. Result Marshalling: Results are converted from Python to JavaScript objects
$3
- Nussinov Algorithm: Dynamic programming for RNA secondary structure prediction
- Turner Energy Model: Simplified free energy calculations
- Quantum-Inspired Sampling: Biased sequence generation for stable structures
Performance
- First Run: ~2-5 seconds (Pyodide initialization)
- Subsequent Runs: <100ms per prediction
- Memory: ~50MB for Pyodide runtime
Limitations
This is a simplified version suitable for:
- Educational purposes
- Rapid prototyping
- Initial candidate screening
- Computational biology research
For production drug discovery, consider using the full Python backend with:
- Full VQE quantum energy calculations
- Molecular docking
- Advanced thermodynamic analysis
- Experimental validation
Migration from v1.x
v1.x (Server mode):
`typescript
const client = new RnaFoldQuantumClient('http://localhost:5000');
const { job_id } = await client.runPipeline(formData);
`
v2.x (Standalone mode):
`typescript
const client = new RnaFoldQuantumClient(); // Default: WASM mode
await client.initialize();
const result = await client.runPipeline({ targetName: 'MyTarget' });
``