NIM for DiffDock
Type: Microservice Tags: NVIDIA, NIM, BioNeMo, DiffDock, scalable molecular docking, protein-ligand docking, pose prediction, drug discovery, diffusion model Related: NVIDIA-BioNeMo, NVIDIA-NIM, NIM-for-MolMIM, NIM-for-GenMol, NIM-for-Boltz2, NIM-for-OpenFold3, NIM-for-ALCHEMI-Batched-Geometry-Relaxation, cuEquivariance, TensorRT, NVIDIA-Clara, NVIDIA-AI-Enterprise, NGC Sources: https://docs.nvidia.com/nim/bionemo/diffdock/latest/overview.html Last Updated: 2026-04-29
Summary
NIM for DiffDock is NVIDIA’s BioNeMo NIM for scalable molecular docking and protein-ligand pose prediction. Current NVIDIA docs describe DiffDock as a generative model that predicts the three-dimensional structure of a protein-ligand complex and ranks sampled poses with confidence estimates.
Detail
Purpose
DiffDock supports drug-discovery workflows that need to predict how a small molecule ligand binds to a protein target. It can sit downstream of molecular generation and upstream of ranking, refinement, or simulation.
Current scope
- Takes protein and molecule 3D structures as input.
- Does not require prior binding-pocket information.
- Uses an equivariant geometric/diffusion approach where ligand position, orientation, and torsion angles can change during denoising.
- Outputs multiple sampled protein-ligand poses with confidence estimates.
- Current docs describe training data from PLINDER and SAIR-derived protein-ligand structure/activity sources.
- Current docs position DiffDock for accurate, computationally efficient docking in AI drug discovery pipelines.
NVIDIA context
DiffDock is the current Scalable Molecular Docking NIM in the NVIDIA NIM index. It connects molecular generation NIMs such as NIM-for-MolMIM and NIM-for-GenMol to structure/affinity workflows such as NIM-for-Boltz2 and atomistic refinement with ALCHEMI NIMs.
Connections
- NIM-for-MolMIM and NIM-for-GenMol - candidate molecule generation NIMs that can feed docking workflows.
- NIM-for-Boltz2 and NIM-for-OpenFold3 - biomolecular complex prediction NIMs adjacent to protein-ligand modeling.
- NIM-for-ALCHEMI-Batched-Geometry-Relaxation - atomistic relaxation NIM for candidate structures.
- cuEquivariance - acceleration context for equivariant/geometric models in molecular docking.
- NVIDIA-BioNeMo and NVIDIA-Clara - life-sciences and drug-discovery platform context.
- NVIDIA-AI-Enterprise, TensorRT, and NGC - production deployment, optimization, and distribution context.
Source Excerpts
- NVIDIA docs describe DiffDock as predicting protein-ligand complex structures for molecular docking or pose prediction.
- The current docs state that DiffDock samples poses and ranks them through a confidence model.