git:20260528.d3d04b2 to git:20260528.6b09274

---

name: "smina-molecular-docking"

description: "smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock-blind-docking; for the Python-bindings/Vinardo workflow use autodock-vina-docking."

license: "GPL-2.0"

---

# smina Molecular Docking

## Overview

## When to Use

## Prerequisites

```bash

command -v smina || conda install -c conda-forge smina openbabel

pip install rdkit prody pandas py3Dmol

```

## Quick Start

```python

import subprocess

result = subprocess.run([

"smina",

"--exhaustiveness", "16",

"--num_modes", "9",

"--seed", "42",

], check=True, capture_output=True, text=True)

```

## Workflow

### Step 1: Prepare the Receptor (PDBQT)

```python

pdb_id = "1HPV"

prody.fetchPDB(pdb_id, compressed=False)

prody.writePDB(f"{pdb_id}_protein.pdb", protein)

receptor_pdbqt = f"{pdb_id}_receptor.pdbqt"

subprocess.run([

"prepare_receptor",

"-r", f"{pdb_id}_protein.pdb",

"-o", receptor_pdbqt,

"-A", "hydrogens",

], check=True)

print(f"Receptor: {receptor_pdbqt} ({protein.numAtoms()} atoms)")

```

```python

from rdkit import Chem

from rdkit.Chem import AllChem

mol = Chem.AddHs(mol)

AllChem.EmbedMolecule(mol, randomSeed=42)

AllChem.MMFFOptimizeMolecule(mol)

print(f"Ligand SDF: candidate.sdf ({mol.GetNumAtoms()} atoms)")

```

### Step 3: Extract a Reference Ligand for Autoboxing

```python

import prody

if ref is None:

prody.writePDB(f"{pdb_id}_ref_ligand.pdb", ref)

```

### Step 4: Run Docking with Autobox

```python

import subprocess

"smina",

"-r", receptor_pdbqt,

"-l", "candidate.sdf",

"--autobox_ligand", f"{pdb_id}_ref_ligand.pdb",

"-o", "candidate_docked.sdf",

"--exhaustiveness", "16",

"--num_modes", "9",

"--energy_range", "3",

"--cpu", "4",

"--seed", "42",

for line in proc.stdout.splitlines()[-15:]:

print(line)

```

```python

from rdkit import Chem

import pandas as pd

rows = []

if mol is None:

continue

df = pd.DataFrame(rows).sort_values("affinity_kcal_mol")

print(df.to_string(index=False))

print(f"Best: {df.iloc[0]['affinity_kcal_mol']:.2f} kcal/mol")

```

### Step 6: Local Minimization of an Existing Pose

```python

import subprocess

subprocess.run([

"smina",

"-r", receptor_pdbqt,

"-l", "candidate_pose.sdf",

"--autobox_ligand", "candidate_pose.sdf", # box around the pose itself

"--autobox_add", "4",

"-o", "candidate_min.sdf",

"--local_only",

"--minimize_iters", "1000",

], check=True, capture_output=True, text=True)

print("Local minimization complete: candidate_min.sdf")

```

### Step 7: Visualize the Docked Complex

```python

import py3Dmol

view = py3Dmol.view(width=800, height=600)

```

### Step 8: Batch Virtual Screening

```python

from rdkit import Chem

from rdkit.Chem import AllChem

library = pd.DataFrame({

"name": ["cpd_001", "cpd_002", "cpd_003"],

})

results = []

for _, row in library.iterrows():

w = Chem.SDWriter(lig_sdf); w.write(mol); w.close()

proc = subprocess.run([

], capture_output=True, text=True)

if proc.returncode != 0:

continue

docked = next(iter(Chem.SDMolSupplier(out_sdf, removeHs=False)))

aff = float(docked.GetProp("minimizedAffinity")) if docked and docked.HasProp("minimizedAffinity") else None

ranked = pd.DataFrame(results).sort_values("affinity_kcal_mol")

ranked.to_csv("screening_results.csv", index=False)

print(ranked.to_string(index=False))

```

## Key Parameters

| Parameter | Default | Range / Options | Effect |

|-----------|---------|-----------------|--------|

## Common Recipes

### Recipe: Score-Only (Single-Point Energy)

```python

import subprocess

proc = subprocess.run([

], capture_output=True, text=True, check=True)

for line in proc.stdout.splitlines():

if line.startswith("Affinity:"):

print(line)

```

### Recipe: Custom Scoring Function

```python

-0.035579 gauss(o=0,_w=0.5,_c=8)

-0.005156 gauss(o=3,_w=2,_c=8)

0.840245 repulsion(o=0,_c=8)

-0.035069 hydrophobic(g=0.5,_b=1.5,_c=8)

-0.587439 non_dir_h_bond(g=-0.7,_b=0,_c=8)

1.923 num_tors_div

subprocess.run([

"--custom_scoring", "my_scoring.txt",

"--autobox_ligand", "ref.pdb", "--autobox_add", "8",

], check=True)

```

### Recipe: Per-Atom Score Decomposition

```python

import subprocess

from rdkit import Chem

subprocess.run([

], check=True)

mol = next(iter(Chem.SDMolSupplier("pose_decomp.sdf", removeHs=False)))

for prop in mol.GetPropNames():

if "atom_term" in prop:

print(f"{prop}: {mol.GetProp(prop)[:120]}")

```

### Recipe: Re-Docking Validation

```python

import subprocess

from rdkit import Chem

from rdkit.Chem import AllChem

subprocess.run([

], check=True)

print(f"Re-docking RMSD: {rmsd:.2f} Å -> {'PASS' if rmsd < 2.0 else 'FAIL'}")

```

## Expected Outputs

## Troubleshooting

| Problem | Cause | Solution |

|---------|-------|----------|

## References