Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.

Deployment Geography:

Global

Use Case:

Boltz-2 NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to predict the three-dimensional structure of biomolecular complexes and predict small-molecule binding affinities. Trained on millions of curated experimental datapoints with a novel training strategy tailored for noisy biochemical assay data, Boltz-2 demonstrates robust performance across hit-discovery, hit-to-lead, and lead optimization.

Release Date:

Build.nvidia.com September 30, 2025 via build.nvidia.com NGC September 30, 2025 via https://registry.ngc.nvidia.com/

References

@article{wohlwend2024boltz,
title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},
author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},
journal = {bioRxiv},
year = {2024},
doi = {10.1101/2024.11.19.624167},
language = "en"
}

Model Architecture:

Architecture Type: Four components — trunk, denoising module (with steering), confidence module, and a new affinity module Network Architecture: PairFormer

Input

Input Type(s): Biomolecular sequences (protein, DNA, RNA), ligand SMILES or CCD strings, molecular modifications, structural constraints, conditioning parameters, optional booleans Input Format(s): Dictionary containing sequence strings, modification records, and constraint parameters Input Parameters: Sequences (strings), predict_affinity(boolean), modifications (list of residue-specific changes), constraints (dictionary of structural parameters) Other Properties Related to Input: Maximum sequence length of 4096 residues per chain. Maximum of 12 input polymers. Maximum of 20 input ligands. Passing boolean options such as predict_affinity will increase the runtime of the request.

Output:

Output Type(s): Structure prediction in mmcif format; scores in numeric arrays; runtime metrics as a dictionary Output Format: mmcif (text file); numeric arrays; scalar numeric values Output Parameters: 3D atomic coordinates, predicted scores, and metadata Other Properties Related to Output: All Boltz-2 scores are returned by default. Runtime metrics are optional.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engines:

• Pytorch, TensorRT

Supported Hardware Platforms:

• NVIDIA Ampere
• NVIDIA Hopper

Supported Operating Systems:

• Linux
• The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

• Boltz2 version 1.3

Training Dataset:

Link: https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K Data Modality: Text Link: Protein Data Bank as used by AlphaFold3 Data Collection Method by dataset: Human Labeling Method by dataset: Human Properties: All Protein Data Bank structures before 2021-09-30 with a resolution of at least 9 Angstroms, preprocessed to match each structure to its sequence. Ligands were processed similarly. All data was cleaned as described in AlphaFold3.

Evaluation Dataset:

Link: Boltz Evaluation Performed on 744 Structures from the Protein Data Bank Data Collection Method by dataset: Human Labeling Method by dataset: Human and Automated Properties : The test and validation datasets were generated by extensive filtering of PDB sequences deposited between 2021-09-30 and 2023-01-13. In total, 593 structures passed filters and were used for validation.

Inference:

• Engine: Pytorch, TensorRT

Test Hardware:

• NVIDIA H100
• NVIDIA A100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here . You are responsible for ensuring for ensuring the physical properties of model-generated molecules are appropriately evaluated, and comply with applicable safety regulations and ethical standards.

BOLTZ2 NIM is optimized to run best on the following compute:

GPU	Total GPU memory	Azure VM compute	#GPUs on VM	Link
A100	80	Standard_NC24ads_A100_v4	1	link
H100	94	STANDARD_NC40ADS_H100_V5	1	link