## Model Overview ### Description: Boltz-2 NIM is a next-generation structural biology foundation model that shows strong performance for both structure and affinity prediction. Boltz-2 is the first deep learning model to approach the accuracy of free energy perturbation (FEP) methods in predicting binding affinities of small molecules and proteins—achieving strong correlations on benchmarks while being nearly 1000× more computationally efficient. Note that binding affinity is not yet available in the NIM, but will be available very soon!
This NIM is ready for commercial use.
### 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. #### License / Terms of Use GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/). Use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [MIT](https://github.com/jwohlwend/boltz?tab=MIT-1-ov-file#readme). **You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.** ### 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 June 10, 2025 via [build.nvidia.com](https://build.nvidia.com/mit/boltz2) NGC June 10, 2025 ### 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:** Generative Neural Network, Transformer Neural Network
**Network Architecture:** PairFormer
**Input Type(s):** Biomolecular sequences (protein, DNA, RNA), ligand SMILES or CCD strings, molecular modifications, structural constraints, conditioning parameters
**Input Format(s):** Dictionary containing sequence strings, modification records, and constraint parameters
**Input Parameters:** Sequences (strings), 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 5 input polymers. Maximum of 5 input ligands.
### 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 and metadata
**Other Properties Related to Output:** Only the best predicted structure is returned. Runtime metrics are optional.
### Software Integration: **Runtime Engine(s):** * PyTorch, TensorRT
**Supported Hardware Microarchitecture Compatibility:**
* NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace
**[Preferred/Supported] Operating System(s):**
* [Linux]
### Model Version(s): Boltz2
## Training & Evaluation: Not Applicable. ### Training Dataset: **Link:** [Protein Data Bank as used by AlphaFold3](https://github.com/jwohlwend/boltz/blob/main/docs/training.md)
** 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](https://github.com/jwohlwend/boltz/blob/main/docs/evaluation.md)
** Data Collection Method by dataset
* Human
** Labeling Method by dataset
* Hybrid: Human and Automated
**Properties:** The test and validation datasets were generated by extensive filtering of PDB sequences deposited between 2021-09-31 and 2023-01-13. In total, 593 structures passed filters and were used for validation.
### Inference: **Acceleration Engine:** PyTorch, TensorRT
**Test Hardware:**
* NVIDIA A100
* NVIDIA L40
* NVIDIA H100
### 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 security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). **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.**