SMINA is a fork of AutoDock Vina developed by David Koes at the University of Pittsburgh. It extends Vina with comprehensive support for custom scoring functions, dramatically faster minimization, and enhanced workflows for scoring function research.
Published in 2013, SMINA was designed to address limitations in Vina's scoring function development capabilities. The tool enables researchers to define custom scoring functions with user-specified weights, making it valuable for both standard docking and advanced scoring research.
SMINA also includes the Vinardo scoring function, an optimized variant that consistently outperforms Vina's default scoring in pose prediction, ranking, and virtual screening benchmarks.
SMINA inherits Vina's core search algorithm while extending its scoring capabilities. The search uses Iterated Local Search with BFGS quasi-Newton optimization, exploring the conformational space through random mutations of position, orientation, and torsion angles.
SMINA provides three scoring options:
Default (Vina) uses the original AutoDock Vina scoring function with Gaussian steric terms, hydrogen bonding, hydrophobic interactions, and a rotatable bond penalty. This is well-validated and suitable for most docking applications.
Vinardo removes Vina's problematic second Gaussian term that contributed 58% of binding energy despite creating artifacts. Vinardo uses optimized atomic radii and simplified terms, achieving 89.7% top-1 docking success versus Vina's 80.5% on the CASF-2013 benchmark.
Custom scoring allows researchers to define their own scoring functions using 58 available interaction terms, including electrostatics, desolvation, Lennard-Jones 4-8 potentials, and simple property counts.
SMINA's custom scoring supports these term types:
gauss), repulsion potentialsEach term accepts parameters for offset, width, cutoff, and exponent, enabling fine-grained control over the scoring function behavior.
SMINA provides four operational modes:
| Mode | Description | Speed |
|---|---|---|
Full docking | Global search + local optimization | Standard |
Minimize only | Refine existing pose without global search | 10-20x faster |
Score only | Evaluate pose without moving atoms | Fastest |
Local only | Local optimization from starting pose | Fast |
The Minimize only mode is particularly valuable for pose refinement workflows, achieving 10-20x speedup over full docking by skipping global search.
Upload a PDB file or enter an RCSB PDB ID. The protein should be prepared with hydrogens added and missing residues fixed. Use PDB Fixer for automated preparation.
Enter a SMILES string for small molecules, or upload an SDF, MOL, or PDBQT file. SMINA automatically generates 3D coordinates and calculates partial charges via OpenBabel.
For complex ligands (macrocycles, peptides, >150 atoms), pre-prepared PDBQT files are recommended to ensure proper handling.
The search box defines where SMINA explores ligand binding:
We recommend using autobox when a reference ligand structure is available, as this focuses the search and improves both speed and accuracy.
Affinity is reported in kcal/mol. More negative values indicate stronger predicted binding:
| Range | Interpretation |
|---|---|
| -4 to -6 | Weak binding |
| -6 to -8 | Moderate binding |
| -8 to -10 | Strong binding |
| < -10 | Very strong binding |
Vinardo scores may differ slightly from Vina due to the modified scoring function. Both use the same scale and interpretation guidelines.
SMINA ranks poses by predicted binding affinity. The top-ranked pose represents the most favorable binding mode, but examining multiple poses is recommended. Alternative binding modes within 1-2 kcal/mol of the best score may be biologically relevant.
On the CASF-2013 benchmark, Vinardo achieves:
r = 0.601 between predicted and experimental binding affinities| Feature | SMINA | AutoDock Vina | GNINA |
|---|---|---|---|
| Scoring | Vina/Vinardo/Custom | Vina | Vina + CNN |
| Max poses | 50 | 20 | 20 |
| Minimization speed | 10-20x faster | Standard | Standard |
| Custom scoring | Yes | No | No |
| Per-atom energies | Yes | No | No |
SMINA is ideal when you need custom scoring functions, fast minimization, or more than 20 poses. For standard docking with known scoring, Vina or GNINA work equally well.
Custom scoring functions are defined as weighted sums of interaction terms. Each line specifies a weight and term with parameters:
1-0.035579 gauss(o=0,_w=0.5,_c=8)2-0.005156 gauss(o=3,_w=2,_c=8)30.840245 repulsion(o=0,_c=8)4-0.035069 hydrophobic(g=0.5,_b=1.5,_c=8)5-0.587439 non_dir_h_bond(g=-0.7,_b=0,_c=8)61.923 num_tors_divParameters include:
o: Offset from optimal distancew: Gaussian widthc: Distance cutoffg: Good distance thresholdb: Bad distance thresholdDetailed term documentation is available in the SMINA GitHub repository.