Accelerate molecular discovery with ultra-fast structure prediction, target docking, and automated conformer intelligence.
Every tool in the pipeline exposes raw data, provenance, and deterministic parameters.
fpocket integration with Voronoi-based druggability scoring
| Volume | Druggability | Hydrophobic | |
|---|---|---|---|
| #1 | 1,240 ų | 0.84 | 62% |
| #2 | 890 ų | 0.61 | 48% |
| #3 | 420 ų | 0.31 | 29% |
Cloud worker pool with exhaustiveness scaling
vina --receptor target.pdbqt \
--ligand ligand.pdbqt \
--exhaustiveness 32
Mode | Affinity (kcal/mol) | RMSD
1 | -9.4 | 0.000
2 | -8.7 | 1.412
3 | -8.1 | 2.105
Automated preparation with OpenBabel integration
Every result carries an auditable, seed-locked execution manifest
{
"seed": 14920481,
"engine": "vina-v1.2.5",
"box_size": [20, 20, 20],
"center": [12.4, -8.1, 34.2],
"exhaustiveness": 32,
"provenance": "verified"
}A continuous, auditable pipeline — no manual handoffs.
Direct upload from RCSB PDB ID or AlphaFold DB.
Instant Voronoi cavity partitioning via fpocket.
Cloud worker pool execution with AutoDock Vina.
Energy-minimized pose ranking and CSV/PDBQT export.
Illustrative target metrics. No hardware or load measurements have been taken yet.
Single-run average docking latency
Ligands per batch virtual screening
Deterministic reproducibility with seed lock
Local CLI dependencies or drivers needed
Full Python SDK, REST API, or point-and-click UI. Same deterministic engine underneath.
import ligentra
client = ligentra.Client(api_key="lg_live_...")
job = client.docking.create(
protein="7K40.pdb",
ligand="CHEMBL25.sdf",
engine="autodock-vina",
exhaustiveness=32
)
print(f"Top Pose: {job.best_affinity} kcal/mol")No credit card required. Full access to the docking engine, structure prediction, and provenance tools.