DiffDock-L

DiffDock webserver overview

The DiffDock webserver runs DiffDock-L, the MIT diffusion model for protein and small-molecule docking, without installation or a GPU. It predicts where and how a ligand binds to a protein without a predefined binding site, and returns ranked binding poses with a confidence score for each.

ProteinIQ runs the official DiffDock release v1.1.3 with the v1.1 (DiffDock-L) models, through the repository's own inference.py and its published default configuration. The receptor can be a structure, a PDB ID, or a protein sequence, which DiffDock folds with ESMFold before docking. For a step-by-step tutorial, benchmarks, and help with errors, see how to use DiffDock online.

What DiffDock-L does

DiffDock treats docking as a generative task. It starts each sample from a random ligand position, orientation, and set of torsion angles, then refines it by reverse diffusion toward a plausible bound pose. A separate confidence model scores every final pose, and the poses are returned in confidence order.

• The protein is held rigid. Backbone and side chains do not move.
• The ligand's 3D geometry is regenerated with RDKit from its chemistry, so uploaded coordinates do not change the result.
• Protein residues are described with ESM-2 language model embeddings, which carry sequence information into the model.
• Each run starts from new random poses. DiffDock has no seed, so repeated runs differ.

DiffDock vs DiffDock-L

DiffDock-L is the second DiffDock release from the same MIT group. It is the model this webserver runs.

	DiffDock	DiffDock-L
Publication	ICLR 2023	ICLR 2024
Parameters	About 20 million	About 30 million
PDBBind top-1 within 2 Å, 10 samples	35.0%	43.0%
DockGen (unseen protein domains), 10 samples	7.1%	22.6%

DiffDock-L added synthetic training complexes, training balanced across protein domain clusters, and confidence bootstrapping, which improved docking to protein classes unlike the training data (Corso et al., 2024). NVIDIA's DiffDock NIM is a separately trained model with its own weights and is not the model used here.

When to use DiffDock

DiffDock fits blind docking, when the binding site is unknown, and quick pose hypotheses for drug-like ligands. Other methods fit other questions:

Question	Better choice
The pocket is known and you need an energy score or a reproducible seed	AutoDock Vina
You need convolutional neural network scoring of prepared 3D ligands	GNINA
The protein is likely to change shape when the ligand binds	DynamicBind or FlowDock
You want protein and ligand predicted together, or an affinity estimate	Boltz-2

Pricing

A DiffDock job costs 50 credits. The price does not change with the number of poses, the diffusion steps, the trajectory option, the receptor size, or whether the receptor is a structure or a sequence. The exact quote is shown before submission.

Inputs

Input	Accepted formats	Limits
Protein (structure)	.pdb, .ent, or an RCSB PDB ID	Up to 3,000 protein residues (1,000 on the Free plan). Files up to 50 MB.
Protein (sequence)	Pasted sequence, .fasta, .fa, or .txt	Folded with ESMFold inside DiffDock. Each FASTA record is one chain.
Ligand	SMILES, .smiles, .sdf, .mol, .mol2, .pdb, .pdbqt, or a PubChem fetch	Up to 150 heavy (non-hydrogen) atoms per ligand. Files up to 10 MB. A multi-record SDF runs one job per record.

The receptor file is used as submitted. DiffDock regenerates the ligand's 3D geometry for every input format. Each receptor chain can contain up to 1,022 residues, with at most 3,000 residues in total. ProteinIQ checks these limits before starting the job because DiffDock truncates the ESM-2 embeddings at 1,022 residues per chain.

ProteinIQ warns for ligands above 100 heavy atoms and rejects ligands above 150 before charging. This is a hosting limit, not a maximum defined by DiffDock. Large or flexible ligands may still fail during conformer preparation below this limit. Contact support to discuss larger ligands.

Settings

Defaults match DiffDock's default_inference_args.yaml.

Docking

Parameter	Type	Default	Description
Number of poses	integer	10	Poses sampled and ranked per job, from 1 to 40.
Inference steps	integer	20	Length of the reverse diffusion schedule, from 10 to 30.
Actual steps	integer	19	Schedule steps executed, from 8 to 30. Cannot exceed Inference steps.
No final step noise	boolean	true	Removes noise from the last executed diffusion step.
Save reverse-diffusion visualization	boolean	false	Writes a trajectory PDB file for each ranked pose.

Advanced sampling

Parameter	Type	Default	Description
Start at a random residue	boolean	false	Places each initial ligand position at a random receptor residue instead of near the protein center.
Initial noise proportion	number	1.4601642460337794	Scale of the initial translation noise, from −10 to 10. Negative values scale the model's maximum translation noise instead of the receptor radius.
Sampling temperature (translation)	number	1.170050527854316	Sampling temperature for translation, from 0 to 20.
Sampling temperature (rotation)	number	2.06391612594481	Sampling temperature for rotation, from 0 to 20.
Sampling temperature (torsion)	number	7.044261621607846	Sampling temperature for torsion angles, from 0 to 20.
Psi temperature (translation)	number	0.727287304570729	Psi temperature parameter for translation, from 0 to 10.
Psi temperature (rotation)	number	0.9022615585677628	Psi temperature parameter for rotation, from 0 to 10.
Psi temperature (torsion)	number	0.5946212391366862	Psi temperature parameter for torsion angles, from 0 to 10.
Sigma data temperature (translation)	number	0.9299802531572672	Sigma data temperature parameter for translation, from 0.01 to 10.
Sigma data temperature (rotation)	number	0.7464326999906034	Sigma data temperature parameter for rotation, from 0.01 to 10.
Sigma data temperature (torsion)	number	0.6943254174849822	Sigma data temperature parameter for torsion angles, from 0.01 to 10.

The advanced defaults are the values the DiffDock authors tuned for the DiffDock-L models.

Outputs

Results open in three views: Viewer shows the receptor with the selected pose, Data lists each pose with its Rank and Confidence, and Files lists every downloadable file.

File	Description
pose_rank01.sdf, pose_rank02.sdf, ...	Docked ligand poses in rank order, unchanged from DiffDock's rank{n}_confidence{c}.sdf files.
protein.pdb	The submitted receptor, for structure inputs.
complex_0_esmfold.pdb	The receptor ESMFold predicted, for sequence inputs.
rank{n}_reverseprocess.pdb	Reverse diffusion trajectory for each ranked pose, when Save reverse-diffusion visualization is on.
native/docking_arrays.npz	Unrounded ligand coordinates and confidence values for every sample.
native/docking_arrays.index.json	Links each ranked pose file to its sample in the arrays.
native/inference_args.yaml	The configuration file passed to DiffDock.
native/inference_invocation.json	The resolved DiffDock arguments, source version, and model checksums.
native/environment.lock.txt	The package versions of the DiffDock environment.
native/run.log	The complete DiffDock log.

The files under native/ record everything needed to describe and reproduce the conditions of a run.

Understanding results

• Rank orders poses by confidence, with rank 1 the most confident.
• Confidence estimates how likely a pose is to lie within 2 Å of the true binding pose. Higher is better. As a rough guide for the top pose, the DiffDock authors treat values above 0 as high, −1.5 to 0 as moderate, and below −1.5 as low confidence, with lower bands for large ligands, multichain proteins, and apo or predicted structures.
• Confidence is not binding affinity and does not check pose geometry. Check selected poses with PoseBusters and rescore with a method such as GNINA when affinity matters.

Limitations

• The receptor is rigid, so induced fit is only captured if the input structure already shows it.
• Accuracy is highest for holo structures and drug-like ligands, and lower for apo, predicted, or very large systems.
• Poses can contain steric clashes or strained geometry even when confidence is high.
• Some rigid polycyclic ligands cannot be embedded by RDKit, and the job then reports that DiffDock could not construct a starting 3D geometry.
• DiffDock is designed for small molecules, not protein-protein or protein-nucleic acid docking.