---
title: 'DiffDock FAQ: binding affinity and confidence scores'
description: 'Does DiffDock predict binding affinity? No — its confidence score predicts geometric accuracy, not affinity. Plus poses, blind docking, reproducibility.'
---

These answers cover what [DiffDock](/docs/diffdock) is, how many poses to request, how to read scores, how it compares to traditional docking, and where the model's accuracy falls off.

## Frequently asked questions

## What is DiffDock?

DiffDock is a diffusion generative model for molecular docking. It treats docking as a generative problem rather than a search/scoring problem. The model learns to reverse a diffusion process that gradually adds noise to ligand poses, enabling it to generate diverse, high-quality binding predictions. The .bloom* platform uses DiffDock-L, the latest version with state-of-the-art success rates on standard benchmarks.

## Does DiffDock use a binding site?

No. This tool runs blind (whole-protein) docking only — there's no parameter to supply a box, a residue range, or a reference ligand. DiffDock's confidence model ranks poses across the full protein surface.

## How many poses should I request?

10 is a good default. Increase if you need diversity; decrease for speed when
triaging many ligands.

## Are scores comparable across proteins?

No. Scores are most meaningful within the same target and setup. Compare poses
for the same protein/box.

## Why are scores low?

Often due to poor ligand geometry or missing protein cleanup. Re-check ligand
sanitization and chain selection.

## Can I reproduce a run?

Not exactly — there's no `seed` parameter, and diffusion sampling is
stochastic, so the same inputs won't produce identical pose coordinates
run to run. Request more poses (`samples_per_complex`) in a single run to
cover that variability instead of relying on re-runs to match.

## What if my protein is large?

Trim to relevant chains/domains. Oversized inputs slow parsing and can reduce
quality.

## Does DiffDock report RMSD to a known pose?

No. There's no reference-ligand input on this tool, so no RMSD against a
known binding mode is computed — validate a pose against a co-crystallized
ligand by visual comparison instead.

## How does DiffDock compare to traditional docking?

Traditional docking methods search for optimal poses by optimizing a physics-based scoring function. DiffDock instead learns the distribution of ligand poses from crystallographic data using diffusion models. On standard benchmarks, DiffDock-L significantly outperforms traditional methods, though performance varies by target class.

## Does DiffDock predict binding affinity?

No. DiffDock's confidence score predicts geometric accuracy (whether the pose is within 2 Angstroms of the true binding mode), not binding affinity. For affinity predictions, use the generated poses as input to separate scoring methods or free energy calculations.

## What are the model's limitations?

DiffDock works best on drug-like small molecules. Performance may decrease for: very large ligands (>100 heavy atoms), highly flexible molecules with many rotatable bonds, covalent binders, metal-coordinating compounds, and proteins with significant induced-fit conformational changes.

## Read next