nvDock

Model Overview Description: All-atom DiffDock Pocket is a diffusion-based model for molecular pocket docking, designed for cases where the protein binding pocket is known in advance. Unlike the original DiffDock, which mainly represents proteins using Cα atoms, the all-atom version models the full atomic structure of the protein pocket. This allows the model to capture more detailed protein–ligand interactions and improves docking accuracy in complex binding environments. The framework includes two components: a Score model and a Confidence model. The Score model generates multiple candidate binding poses for a ligand inside the target pocket through a reverse diffusion process, while the Confidence model ranks these poses to identify the most reliable binding configuration. The model is built on the neural network architecture introduced in the original DiffDock and is trained on curated protein–ligand complex datasets. By using all-atom protein representations together with pocket-focused training, the model can better learn local structural and chemical interactions within the binding site while maintaining efficient inference for docking tasks. This model is available for both commercial and non-commercial use. License/Terms of Use: Use of the model is governed by the NVIDIA Open Model Agreement. Use of source code is governed by Apache License, Version 2.0. ADDITIONAL INFORMATION: MIT License. Deployment Geography: Global Use Case: All-atom DiffDock Pocket is used when the binding site of a protein is already known. It helps predict how a small molecule is likely to fit into that pocket. The model generates multiple binding poses and ranks them to highlight the most reasonable ones. This allows researchers to quickly evaluate candidate compounds before running expensive simulations or experiments. As a result, docking and early screening become faster and more practical. Release Date: GitHub 07/10/2026 via URL Hugging Face 07/10/2026 via URL Model Architecture: