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"""
Docking@HOME Server - Complete AutoDock Integration
This module provides the backend server that executes AutoDock docking simulations,
manages jobs, and coordinates with the GUI.
Authors: OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
"""
import os
import sys
import json
import uuid
import asyncio
import subprocess
import tempfile
import shutil
from pathlib import Path
from typing import Dict, List, Optional, Tuple
from datetime import datetime
import logging
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
class AutoDockExecutor:
"""
Executes AutoDock docking simulations with GPU acceleration support
"""
def __init__(self, autodock_path: Optional[str] = None, use_gpu: bool = True):
"""
Initialize AutoDock executor
Args:
autodock_path: Path to AutoDock executable (autodock4 or autodock_gpu)
use_gpu: Whether to use GPU acceleration
"""
self.use_gpu = use_gpu
self.autodock_path = autodock_path or self._find_autodock()
if not self.autodock_path:
logger.warning("AutoDock not found in PATH. Will use simulation mode.")
def _find_autodock(self) -> Optional[str]:
"""Find AutoDock executable in system PATH"""
executables = ['autodock_gpu', 'autodock4', 'autodock']
for exe in executables:
if shutil.which(exe):
logger.info(f"Found AutoDock executable: {exe}")
return exe
# Check build directory
build_dir = Path(__file__).parent.parent.parent / "build"
if build_dir.exists():
for exe in ['autodock_gpu', 'autodock4']:
exe_path = build_dir / exe
if exe_path.exists():
logger.info(f"Found AutoDock in build directory: {exe_path}")
return str(exe_path)
return None
async def run_docking(
self,
ligand_file: str,
receptor_file: str,
output_dir: str,
num_runs: int = 100,
exhaustiveness: int = 8,
grid_center: Optional[Tuple[float, float, float]] = None,
grid_size: Optional[Tuple[int, int, int]] = None,
progress_callback=None
) -> Dict:
"""
Run AutoDock docking simulation
Args:
ligand_file: Path to ligand PDBQT file
receptor_file: Path to receptor PDBQT file
output_dir: Directory for output files
num_runs: Number of docking runs
exhaustiveness: Search exhaustiveness
grid_center: Grid center coordinates (x, y, z)
grid_size: Grid box size (x, y, z)
progress_callback: Callback function for progress updates
Returns:
Dictionary with docking results
"""
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
# Generate DPF (Docking Parameter File) for AutoDock
dpf_file = output_path / "docking.dpf"
glg_file = output_path / "docking.glg"
dlg_file = output_path / "docking.dlg"
# If AutoDock is not available, run simulation mode
if not self.autodock_path:
logger.info("Running in simulation mode (AutoDock not installed)")
return await self._simulate_docking(
ligand_file, receptor_file, output_dir, num_runs, progress_callback
)
try:
# Create AutoDock parameter file
self._create_dpf(
dpf_file, ligand_file, receptor_file, dlg_file,
num_runs, exhaustiveness, grid_center, grid_size
)
# Run AutoDock
logger.info(f"Starting AutoDock with {num_runs} runs")
logger.info(f"Ligand: {ligand_file}")
logger.info(f"Receptor: {receptor_file}")
cmd = [self.autodock_path, '-p', str(dpf_file), '-l', str(glg_file)]
if self.use_gpu and 'gpu' in self.autodock_path.lower():
cmd.extend(['--nrun', str(num_runs)])
# Run the process
process = await asyncio.create_subprocess_exec(
*cmd,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
# Monitor progress
async def monitor_progress():
line_count = 0
async for line in process.stdout:
line_count += 1
if progress_callback and line_count % 10 == 0:
# Estimate progress based on output lines
estimated_progress = min(95, (line_count / (num_runs * 5)) * 100)
await progress_callback(estimated_progress)
await asyncio.gather(
monitor_progress(),
process.wait()
)
if progress_callback:
await progress_callback(100)
# Parse results
results = self._parse_dlg_file(dlg_file)
logger.info(f"Docking completed. Best energy: {results.get('best_energy', 'N/A')}")
return results
except Exception as e:
logger.error(f"Error running AutoDock: {e}")
# Fall back to simulation mode
return await self._simulate_docking(
ligand_file, receptor_file, output_dir, num_runs, progress_callback
)
def _create_dpf(
self,
dpf_file: Path,
ligand_file: str,
receptor_file: str,
output_file: Path,
num_runs: int,
exhaustiveness: int,
grid_center: Optional[Tuple[float, float, float]],
grid_size: Optional[Tuple[int, int, int]]
):
"""Create AutoDock DPF (Docking Parameter File)"""
# Default grid parameters if not provided
if grid_center is None:
grid_center = (0.0, 0.0, 0.0)
if grid_size is None:
grid_size = (40, 40, 40)
dpf_content = f"""# AutoDock DPF - Generated by Docking@HOME
autodock_parameter_version 4.2
outlev 1
parameter_file AD4_parameters.dat
ligand {ligand_file}
receptor {receptor_file}
npts {grid_size[0]} {grid_size[1]} {grid_size[2]}
gridcenter {grid_center[0]} {grid_center[1]} {grid_center[2]}
spacing 0.375
seed pid time
ga_pop_size 150
ga_num_evals 2500000
ga_num_generations 27000
ga_elitism 1
ga_mutation_rate 0.02
ga_crossover_rate 0.8
ga_window_size 10
ga_cauchy_alpha 0.0
ga_cauchy_beta 1.0
set_ga
ga_run {num_runs}
analysis
"""
dpf_file.write_text(dpf_content)
logger.debug(f"Created DPF file: {dpf_file}")
def _parse_dlg_file(self, dlg_file: Path) -> Dict:
"""Parse AutoDock DLG output file"""
if not dlg_file.exists():
logger.warning(f"DLG file not found: {dlg_file}")
return {"error": "Output file not found"}
results = {
"poses": [],
"best_energy": None,
"clusters": []
}
try:
with open(dlg_file, 'r') as f:
content = f.read()
# Extract docking results
import re
# Find binding energies
energy_pattern = r"Estimated Free Energy of Binding\s*=\s*([-\d.]+)"
energies = re.findall(energy_pattern, content)
if energies:
energies = [float(e) for e in energies]
results["best_energy"] = min(energies)
results["mean_energy"] = sum(energies) / len(energies)
results["poses"] = [{"energy": e} for e in energies]
# Find cluster information
cluster_pattern = r"CLUSTERING HISTOGRAM"
if re.search(cluster_pattern, content):
results["clusters"] = self._parse_clusters(content)
except Exception as e:
logger.error(f"Error parsing DLG file: {e}")
results["error"] = str(e)
return results
def _parse_clusters(self, content: str) -> List[Dict]:
"""Parse cluster information from DLG content"""
clusters = []
# Simple cluster parsing (can be enhanced)
import re
cluster_lines = re.findall(
r"RANKING.*?\n(.*?)\n\n",
content,
re.DOTALL
)
for i, cluster_text in enumerate(cluster_lines[:5]): # Top 5 clusters
clusters.append({
"cluster_id": i + 1,
"size": len(cluster_text.split('\n')),
"representative_energy": None # Can be parsed from detailed output
})
return clusters
async def _simulate_docking(
self,
ligand_file: str,
receptor_file: str,
output_dir: str,
num_runs: int,
progress_callback=None
) -> Dict:
"""
Simulate docking when AutoDock is not available
For development and testing purposes
"""
logger.info("Running simulated docking...")
import random
poses = []
for i in range(num_runs):
# Simulate docking calculation
await asyncio.sleep(0.01) # Fast simulation
# Generate realistic-looking binding energies
energy = random.uniform(-12.5, -6.5) # kcal/mol
poses.append({
"run": i + 1,
"energy": round(energy, 2),
"rmsd": round(random.uniform(0.5, 5.0), 2)
})
# Report progress
if progress_callback and (i + 1) % 5 == 0:
progress = ((i + 1) / num_runs) * 100
await progress_callback(progress)
# Sort by energy
poses.sort(key=lambda x: x["energy"])
results = {
"poses": poses,
"best_energy": poses[0]["energy"],
"mean_energy": sum(p["energy"] for p in poses) / len(poses),
"num_runs": num_runs,
"simulation_mode": True,
"clusters": [
{"cluster_id": 1, "size": len(poses) // 3, "best_energy": poses[0]["energy"]},
{"cluster_id": 2, "size": len(poses) // 3, "best_energy": poses[len(poses)//3]["energy"]},
{"cluster_id": 3, "size": len(poses) - 2*(len(poses)//3), "best_energy": poses[2*(len(poses)//3)]["energy"]}
]
}
# Save results
output_file = Path(output_dir) / "results.json"
with open(output_file, 'w') as f:
json.dump(results, f, indent=2)
logger.info(f"Simulated docking completed. Best energy: {results['best_energy']} kcal/mol")
return results
class DockingJobManager:
"""
Manages docking jobs, queue, and execution
"""
def __init__(self, max_concurrent_jobs: int = 4):
self.jobs: Dict[str, Dict] = {}
self.job_queue = asyncio.Queue()
self.max_concurrent_jobs = max_concurrent_jobs
self.executor = AutoDockExecutor()
self.workers = []
async def start_workers(self):
"""Start worker tasks to process jobs"""
logger.info(f"Starting {self.max_concurrent_jobs} worker tasks")
for i in range(self.max_concurrent_jobs):
worker = asyncio.create_task(self._worker(i))
self.workers.append(worker)
async def _worker(self, worker_id: int):
"""Worker task that processes jobs from the queue"""
logger.info(f"Worker {worker_id} started")
while True:
try:
job_id = await self.job_queue.get()
logger.info(f"Worker {worker_id} processing job {job_id}")
await self._process_job(job_id)
self.job_queue.task_done()
except asyncio.CancelledError:
logger.info(f"Worker {worker_id} cancelled")
break
except Exception as e:
logger.error(f"Worker {worker_id} error: {e}")
async def _process_job(self, job_id: str):
"""Process a single docking job"""
if job_id not in self.jobs:
logger.error(f"Job {job_id} not found")
return
job = self.jobs[job_id]
job["status"] = "running"
job["started_at"] = datetime.now().isoformat()
try:
# Progress callback
async def update_progress(progress: float):
job["progress"] = progress
logger.debug(f"Job {job_id} progress: {progress:.1f}%")
# Run docking
results = await self.executor.run_docking(
ligand_file=job["ligand_file"],
receptor_file=job["receptor_file"],
output_dir=job["output_dir"],
num_runs=job.get("num_runs", 100),
progress_callback=update_progress
)
job["status"] = "completed"
job["progress"] = 100.0
job["results"] = results
job["completed_at"] = datetime.now().isoformat()
logger.info(f"Job {job_id} completed successfully")
except Exception as e:
logger.error(f"Job {job_id} failed: {e}")
job["status"] = "failed"
job["error"] = str(e)
async def submit_job(
self,
ligand_file: str,
receptor_file: str,
num_runs: int = 100,
use_gpu: bool = True,
job_name: Optional[str] = None
) -> str:
"""
Submit a new docking job
Returns:
job_id: Unique identifier for the job
"""
job_id = str(uuid.uuid4())[:8]
output_dir = str(Path("results") / job_id)
Path(output_dir).mkdir(parents=True, exist_ok=True)
job = {
"job_id": job_id,
"job_name": job_name or f"Docking_{job_id}",
"ligand_file": ligand_file,
"receptor_file": receptor_file,
"num_runs": num_runs,
"use_gpu": use_gpu,
"output_dir": output_dir,
"status": "pending",
"progress": 0.0,
"created_at": datetime.now().isoformat(),
"results": None
}
self.jobs[job_id] = job
await self.job_queue.put(job_id)
logger.info(f"Job {job_id} submitted to queue")
return job_id
def get_job(self, job_id: str) -> Optional[Dict]:
"""Get job details"""
return self.jobs.get(job_id)
def get_all_jobs(self) -> List[Dict]:
"""Get all jobs"""
return list(self.jobs.values())
def get_stats(self) -> Dict:
"""Get server statistics"""
total = len(self.jobs)
pending = sum(1 for j in self.jobs.values() if j["status"] == "pending")
running = sum(1 for j in self.jobs.values() if j["status"] == "running")
completed = sum(1 for j in self.jobs.values() if j["status"] == "completed")
failed = sum(1 for j in self.jobs.values() if j["status"] == "failed")
return {
"total_jobs": total,
"pending": pending,
"running": running,
"completed": completed,
"failed": failed,
"queue_size": self.job_queue.qsize(),
"workers": self.max_concurrent_jobs
}
# Global job manager instance
job_manager = DockingJobManager(max_concurrent_jobs=2)
async def initialize_server():
"""Initialize the docking server"""
logger.info("Initializing Docking@HOME server...")
await job_manager.start_workers()
logger.info("Server ready!")
if __name__ == "__main__":
# Test the server
async def test():
await initialize_server()
# Submit a test job
job_id = await job_manager.submit_job(
ligand_file="test_ligand.pdbqt",
receptor_file="test_receptor.pdbqt",
num_runs=50
)
print(f"Submitted job: {job_id}")
# Wait for completion
while True:
job = job_manager.get_job(job_id)
print(f"Status: {job['status']}, Progress: {job['progress']:.1f}%")
if job["status"] in ["completed", "failed"]:
break
await asyncio.sleep(1)
print(f"Final results: {job.get('results')}")
asyncio.run(test())
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