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#!/usr/bin/env python3
"""
Example: Using Docking@HOME Python API
Authors: OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
"""
import asyncio
from pathlib import Path
import sys
# Add parent directory to path if running standalone
sys.path.insert(0, str(Path(__file__).parent.parent / "python"))
from docking_at_home.cli import console
async def main():
"""Example workflow using Python API"""
console.print("[bold]Docking@HOME Python API Example[/bold]\n")
# Example 1: Submit a docking job
console.print("[cyan]Example 1: Submitting a docking job[/cyan]")
job_config = {
"ligand": "examples/data/ligand.pdbqt",
"receptor": "examples/data/receptor.pdbqt",
"num_runs": 100,
"use_gpu": True,
"distributed": False
}
console.print(f"Configuration: {job_config}")
job_id = "EXAMPLE_JOB_001"
console.print(f"Job ID: {job_id}\n")
# Example 2: Monitor job progress
console.print("[cyan]Example 2: Monitoring job progress[/cyan]")
progress_info = {
"status": "running",
"progress": 0.65,
"runs_completed": 65,
"total_runs": 100,
"time_elapsed": 120.5,
"estimated_completion": 185.0
}
console.print(f"Status: {progress_info['status']}")
console.print(f"Progress: {progress_info['progress']*100:.1f}%")
console.print(f"Time elapsed: {progress_info['time_elapsed']:.1f}s\n")
# Example 3: Retrieve and analyze results
console.print("[cyan]Example 3: Analyzing results[/cyan]")
results = {
"job_id": job_id,
"total_poses": 100,
"unique_clusters": 12,
"best_binding_energy": -8.45,
"top_poses": [
{"rank": 1, "energy": -8.45, "rmsd": 0.85},
{"rank": 2, "energy": -8.23, "rmsd": 1.12},
{"rank": 3, "energy": -7.98, "rmsd": 1.45},
{"rank": 4, "energy": -7.76, "rmsd": 1.89},
{"rank": 5, "energy": -7.54, "rmsd": 2.01},
]
}
console.print(f"Total poses generated: {results['total_poses']}")
console.print(f"Unique clusters: {results['unique_clusters']}")
console.print(f"Best binding energy: {results['best_binding_energy']} kcal/mol")
console.print("\n[bold]Top 5 poses:[/bold]")
for pose in results['top_poses']:
console.print(
f" Rank {pose['rank']}: "
f"Energy = {pose['energy']:.2f} kcal/mol, "
f"RMSD = {pose['rmsd']:.2f} Å"
)
# Example 4: Using Cloud Agents for optimization
console.print("\n[cyan]Example 4: AI-powered task optimization[/cyan]")
try:
from src.cloud_agents.orchestrator import CloudAgentsOrchestrator, Task, ComputeNode
orchestrator = CloudAgentsOrchestrator()
await orchestrator.initialize()
# Register compute nodes
node1 = ComputeNode(
node_id="node_gpu_01",
cpu_cores=16,
gpu_available=True,
gpu_type="RTX 3090",
memory_gb=64
)
orchestrator.register_node(node1)
# Submit tasks
task = Task(
task_id="task_001",
ligand_file="ligand.pdbqt",
receptor_file="receptor.pdbqt",
priority="high"
)
orchestrator.submit_task(task)
# Get system statistics
stats = orchestrator.get_system_statistics()
console.print(f"Active nodes: {stats['active_nodes']}")
console.print(f"GPU nodes: {stats['gpu_nodes']}")
console.print(f"Total tasks: {stats['total_tasks']}")
except ImportError:
console.print("[yellow]Cloud Agents module not available[/yellow]")
console.print("\n[bold green]Example completed successfully![/bold green]")
if __name__ == "__main__":
asyncio.run(main())
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