bio-virtual-screening

name	bio-virtual-screening
description	Performs structure-based virtual screening using AutoDock Vina 1.2 for molecular docking. Prepares receptor PDBQT files, generates ligand conformers, defines binding site boxes, and ranks compounds by predicted binding affinity. Use when screening chemical libraries against a protein structure to find potential binders.
tool_type	python
primary_tool	vina

Virtual Screening

Screen compound libraries against protein targets using molecular docking.

Receptor Preparation

from rdkit import Chem
from rdkit.Chem import AllChem
import subprocess

def prepare_receptor(pdb_file, output_pdbqt, remove_waters=True, add_hydrogens=True):
    '''
    Prepare protein for docking.
    Steps: Remove waters -> Add hydrogens -> Assign charges -> PDBQT
    '''
    # Read PDB
    with open(pdb_file) as f:
        lines = f.readlines()

    # Remove waters if requested
    if remove_waters:
        lines = [l for l in lines if not l.startswith(('HETATM', 'CONECT'))
                 or 'HOH' not in l]

    # Write cleaned PDB
    clean_pdb = pdb_file.replace('.pdb', '_clean.pdb')
    with open(clean_pdb, 'w') as f:
        f.writelines(lines)

    # Use Open Babel for conversion (adds hydrogens and charges)
    subprocess.run([
        'obabel', clean_pdb,
        '-O', output_pdbqt,
        '-p', '7.4',  # Add hydrogens at pH 7.4
        '--partialcharge', 'gasteiger'
    ], check=True)

    return output_pdbqt

Ligand Preparation

from rdkit import Chem
from rdkit.Chem import AllChem

def prepare_ligand(smiles, output_pdbqt):
    '''
    Prepare ligand for docking.
    Steps: Generate 3D -> Minimize -> Assign charges -> PDBQT
    '''
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol)

    # Generate 3D conformer (ETKDGv3 is default in modern RDKit)
    AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())

    # Minimize with MMFF
    AllChem.MMFFOptimizeMolecule(mol)

    # Write to MOL file
    mol_file = output_pdbqt.replace('.pdbqt', '.mol')
    Chem.MolToMolFile(mol, mol_file)

    # Convert to PDBQT with Open Babel
    subprocess.run([
        'obabel', mol_file,
        '-O', output_pdbqt,
        '--partialcharge', 'gasteiger'
    ], check=True)

    return output_pdbqt

Docking with Vina

from vina import Vina

def dock_ligand(receptor_pdbqt, ligand_pdbqt, center, box_size, exhaustiveness=8):
    '''
    Dock a single ligand using AutoDock Vina 1.2.

    Args:
        receptor_pdbqt: Prepared receptor file
        ligand_pdbqt: Prepared ligand file
        center: (x, y, z) center of binding site
        box_size: (x, y, z) box dimensions (Angstroms)
        exhaustiveness: Search thoroughness (8=quick, 32=production, 64=thorough)
    '''
    v = Vina(sf_name='vina')
    v.set_receptor(receptor_pdbqt)
    v.set_ligand_from_file(ligand_pdbqt)

    # Define search space
    # Box size generally < 30x30x30 Angstroms
    v.compute_vina_maps(center=center, box_size=box_size)

    # Dock
    v.dock(exhaustiveness=exhaustiveness, n_poses=10)

    # Get results
    energies = v.energies()  # List of (affinity, rmsd_lb, rmsd_ub)
    poses = v.poses()  # PDBQT string of all poses

    return energies, poses

# Example usage
# center = (10.0, 20.0, 30.0)  # Binding site center
# box = (20, 20, 20)  # Box size in Angstroms
# energies, poses = dock_ligand('receptor.pdbqt', 'ligand.pdbqt', center, box)

Virtual Screening Pipeline

import os
from pathlib import Path
from vina import Vina
import pandas as pd

def virtual_screen(receptor_pdbqt, ligand_smiles_dict, center, box_size,
                   output_dir, exhaustiveness=8, n_poses=3):
    '''
    Screen compound library against receptor.

    Args:
        receptor_pdbqt: Prepared receptor
        ligand_smiles_dict: Dict of {name: smiles}
        center: Binding site center
        box_size: Search box size
        output_dir: Directory for output files
        exhaustiveness: Search thoroughness
        n_poses: Number of poses to save per ligand
    '''
    Path(output_dir).mkdir(parents=True, exist_ok=True)

    v = Vina(sf_name='vina')
    v.set_receptor(receptor_pdbqt)
    v.compute_vina_maps(center=center, box_size=box_size)

    results = []

    for name, smiles in ligand_smiles_dict.items():
        try:
            # Prepare ligand
            ligand_pdbqt = f'{output_dir}/{name}.pdbqt'
            prepare_ligand(smiles, ligand_pdbqt)

            # Dock
            v.set_ligand_from_file(ligand_pdbqt)
            v.dock(exhaustiveness=exhaustiveness, n_poses=n_poses)

            energies = v.energies()
            best_affinity = energies[0][0] if energies else None

            # Save poses
            if energies:
                pose_file = f'{output_dir}/{name}_poses.pdbqt'
                v.write_poses(pose_file, n_poses=n_poses)

            results.append({
                'name': name,
                'smiles': smiles,
                : best_affinity,
                : pose_file  energies  
            })

         Exception  e:
            ()
            results.append({
                : name,
                : smiles,
                : ,
                : (e)
            })

    results_df = pd.DataFrame(results)
    results_df = results_df.sort_values()

     results_df

Binding Site Definition

def find_binding_site(receptor_pdb, ligand_pdb=None, padding=5.0):
    '''
    Define binding site from co-crystallized ligand or protein center.

    Args:
        receptor_pdb: Protein PDB file
        ligand_pdb: Optional co-crystallized ligand
        padding: Angstroms to add around ligand
    '''
    if ligand_pdb:
        # Center on ligand
        from rdkit import Chem
        mol = Chem.MolFromPDBFile(ligand_pdb)
        conf = mol.GetConformer()
        coords = [conf.GetAtomPosition(i) for i in range(mol.GetNumAtoms())]

        x = [c.x for c in coords]
        y = [c.y for c in coords]
        z = [c.z for c in coords]

        center = (sum(x)/len(x), sum(y)/len(y), sum(z)/len(z))
        box_size = (max(x)-min(x)+2*padding, max(y)-min(y)+2*padding, max(z)-min(z)+2*padding)
    else:
        # Use protein center (not recommended)
        center = (0, 0, 0)
        box_size = (30, 30, 30)

    return center, box_size

Related Skills

• molecular-io - Load and convert molecules
• admet-prediction - Filter before docking
• structural-biology/structure-io - Protein structure handling
• structural-biology/modern-structure-prediction - Generate targets