> This page is for DiffDock, version 2.0.1.
> For other versions, use one of these documentation indexes:
> - 2.3.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/diffdock/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/diffdock/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/diffdock/2.1.0/llms.txt
> - 2.0.1: https://docs.nvidia.com/nim/bionemo/diffdock/2.0.1/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/diffdock/1.2.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Overview

[DiffDock](https://openreview.net/pdf?id=kKF8_K-mBbS) is a state-of-the-art generative model used for drug discovery that predicts the three-dimensional structure of a protein-ligand complex, a crucial step in the drug discovery process. It predicts the binding structure of a small molecule ligand to a protein, known as molecular docking or pose prediction.

DiffDock can:

* Helps AI drug discovery pipelines and opens new research avenues for downstream task integrations.
* Highly accurate and computationally efficient
* Fast inference times and provides confidence estimates with high selective accuracy.

![Example output](/nim/bionemo/_fern-files/_img/c884df8353da94d82a9ff3faaaa2ee9a8a2be65bdb0dfee7b90dbe80eb211b00.webp)

<Note>
A more detailed description of the model can be found in the [Model Card](https://build.nvidia.com/mit/diffdock/modelcard).
</Note>

DiffDock is an equivariant geometric model for blind molecular docking pose estimation. It requires protein and molecule 3D structures as input and does not require any information about a binding pocket. During its diffusion process, the molecule's position relative to the protein, its orientation, and the torsion angles can change. By running the learned reverse diffusion process, it transforms a distribution of noisy prior molecule poses to the one learned by the
model. As a result, it outputs many sampled poses and ranks them via its confidence model.