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Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement

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Model Overview

Description:

Proteina-Complexa is a state-of-the-art generative model that designs fully atomistic protein structures for binder design to both protein and small molecule targets, generating both the sequence and all atomic coordinates. It is trained using a partially latent flow matching objective, where the protein backbone is modeled explicitly while side-chain details and sequence information are captured in a fixed-size latent space. New proteins are generated iteratively starting from random noise, using stochastic sampling. This framework enables a protein designer to generate novel, fully atomistic protein structures conditioned on target structures to generate binding proteins for diverse use cases. Its ability to perform motif scaffolding at the same time also allows applications in enzyme design.

This model is ready for commercial use.

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. The model will be released on GitHub on March 15 under the Apache 2.0 open-source license.

Deployment Geography:

Global.

Use Case:

Proteina-Complexa can be used by protein designers interested in generating novel fully atomistic protein binder structures and their corresponding sequences.

Release Date:

Github [03/15/2026] via [https://github.com/NVIDIA-Digital-Bio/Proteina-Complexa]\nNGC [03/16/2026] via [https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/proteina_complexa]\nHugging Face [03/15/2026] via :\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-Protein-Target-160M-v1]
\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-Ligand-Target-160M-v1]
\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-AME-160M-v1]

References(s):

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Model Architecture:

Architecture Type: Autoencoder + Flow model.\nNetwork Architecture: Transformer.\nProteina-Complexa uses three neural networks, an encoder, a decoder, and a denoiser, all of which share a core non-equivariant transformer architecture with pair-biased attention mechanisms. For refining the pair representation, optional triangle multiplicative layers can be included within the denoiser network. The architecture operates on a partially latent representation, explicitly modeling the protein's three-dimensional alpha-carbon coordinates while capturing the sequence and all other atomistic details in per-residue eight-dimensional latent variables. The denoiser network parametrizes the flow that maps a noise distribution to the joint distribution of alpha-carbon coordinates and latent variables, which are iteratively updated during the generation process. The denoiser also takes as inputs three-dimensional target structure and sequence for protein or small molecule targets that are then used as conditioning for the design of the binding protein. The decoder then generates the final fully atomistic binder structure from these outputs.

This model was developed based on: La-Proteina (https://github.com/NVIDIA-Digital-Bio/la-proteina).
\nNumber of model parameters: 1.6*10^8

Input:

Input Type(s):

Text (time step schedules, noise schedules, sampling modes, motif coordinates, target coordinates)
Number (number of residues, noise scales, time step sizes, seed, noise schedule exponents)
Binary (use of self-conditioning)

Input Format:

Text: Strings (time step schedules, noise schedules, sampling modes), PDB file (motif coordinates)
Number: Integers (number of residues, seed), floats (noise scales, time step sizes, noise schedule exponents)
Binary: Booleans

Input Parameters:

Text: One-dimensional (1D) or text file (PDB file)
Number: One-dimensional (1D)
Binary: One-dimensional (1D)

Output:

Output Type(s): Text (generated atomistic coordinates and sequence)
\nOutput Format: Text: PDB file (generated protein with sequence and all atom coordinates)
\nOutput Parameters: One-dimensional (1D)
\nOther Properties Related to Output: The model output is stored as a PDB file containing the protein sequence and three-dimensional coordinates for all atoms.

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 Engine(s):

[Pytorch]
\nSupported Hardware Microarchitecture Compatibility:
[NVIDIA Ampere]

Preferred/Supported Operating System(s):

[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):

We release ten model checkpoints, seven for latent diffusion models and three for the corresponding autoencoders.\nLatent diffusion checkpoints (used together with the corresponding autoencoder below):

NV-Proteina-Complexa-Protein-Target-160M-v1
NV-Proteina-Complexa-Ligand-Target-160M-v1
NV-Proteina-Complexa-AME-160M-v1\n
\n

Training, Testing, and Evaluation Datasets:

Dataset Overview:

Total Size: Approximately 1,300,000 data points
\nTotal Number of Datasets: 4 (AFDB, Teddymer, PDB and PLINDER)\nDataset partition: Training 99.9%, Validation 0.1%
\nTime period for training data collection: The AFDB was generated using Alpha Fold 2, published in 2021, the PDB consists of experimental data, which started being collected in 1971.\nTime period for testing data collection: See above.\nTime period for validation data collection: See above.

Training Dataset:

(1) AFDB Link: https://alphafold.ebi.ac.uk/

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nSynthetic (AlphaFold predictions)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): The AlphaFold Protein Structure Database (AFDB) contains approximately 214M synthetic three-dimensional protein structures predicted by AlphaFold2, along with their corresponding sequences. We trained Complexa models on a subset of the AFDB, one comprising 344.508 structures.

(2) Teddymer:

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nSynthetic (AlphaFold predictions)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): The Teddymer dataset contains around 500,000 synthetic dimer pairs created by annotating, filtering and cropping structure predictions from the AFDB.

(3) PDB Link: https://www.rcsb.org/

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nNot Applicable: Experimental structure determination (X-ray crystallography, cryo-EM, NMR)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): We use a filtered subset of the PDB during model training.

(4) ** PLINDER Link:** https://console.cloud.google.com/storage/browser/plinder

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nNot Applicable: Experimental structure determination (X-ray crystallography, cryo-EM, NMR)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): We use a filtered subset of PLINDER (around 80,000 structures) as training set for our ligand models.

Testing Dataset:

(1) AFDB\nLink: https://alphafold.ebi.ac.uk/

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nSynthetic (AlphaFold predictions)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): We use a subset of the AFDB subset of 734,658 structures as a reference set in evaluations. We also extract a subset of files, and modify them to create the benchmark for binder design. We release these modified files with the codebase.

(2) PDB\nLink: https://www.rcsb.org/

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nSynthetic (AlphaFold predictions)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): We use the entire PDB as reference set in evaluations. We also extract a subset of files, and modify them to create the benchmark for binder design. We release these modified files with the codebase.

Evaluation Dataset:

Link: https://alphafold.ebi.ac.uk/

Data Modality:

Text (PDB files)

Non-Audio, Image, Text Training Data Size:

Between 0.5MB and 2MB per sample (PDB file)

Data Collection Method by dataset:
\nSynthetic (AlphaFold predictions)

Labeling Method by dataset: N/A

Properties (Quantity, Dataset Descriptions, Sensor(s)): We use a subset of the AFDB subset of 1,300 structures as a validation set during training.

Quantitative Evaluation Benchmarks

Small molecule targets:

Quantitative evaluation of generative performance on four small-molecule binders (SAM, OQO, FAD, IAI) without test-time optimization.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	Time (s)	Novelty	SAM unique successes	OQO unique successes	FAD unique successes	IAI unique successes
RFDiffusion-AllAtom	2	0.72	2	3	5	8
Proteina-Complexa (ours)	10	0.71	10	6	17	19

Protein targets

Quantitative evaluation of generative performance on protein binder targets without test-time optimization, comparing Complexa to prior generative baselines.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	Time (s)	Novelty	Self unique successes	MPNN-FI unique successes	MPNN unique successes
RFDiffusion	4.68	0.87	3	70.8	–
Protpardelle-1c	0.73	0.77	0	8.13	–
APM	0.31	0.86	1	73.1	3.15
Proteina-Complexa (ours)	9.10	0.80	14	15.6	14.4

Inference-time optimization

Quantitative evaluation of beam-search–based test-time optimization with different folding and hydrogen-bond rewards.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Configuration	Unique successes (avg.)	Interface H-bonds (avg.)
Proteina-Complexa, no reward	77.00	5.271
Proteina-Complexa, with f_ipAE	83.36	5.524
Proteina-Complexa, with f_H-Bond	82.36	7.154
Proteina-Complexa, with f_ipAE + f_H-Bond	86.26	6.518

For more information: Here

Inference:

Acceleration Engine: Pytorch
\nTest Hardware: A100
\nTest Hardware: 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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### Use Case:
\n Proteina-Complexa can be used by protein designers interested in generating novel fully atomistic protein binder structures and their corresponding sequences.
\n\n### Release Date:
\n**Github** [03/15/2026] via [https://github.com/NVIDIA-Digital-Bio/Proteina-Complexa]\n**NGC** [03/16/2026] via [https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/proteina_complexa] \n**Hugging Face** [03/15/2026] via :\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-Protein-Target-160M-v1]
\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-Ligand-Target-160M-v1]
\n[https://huggingface.co/nvidia/NV-Proteina-Complexa-AME-160M-v1]
\n\n## References(s):\n[Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute](https://openreview.net/forum?id=qmCpJtFZra)
\n\n## Model Architecture:\n**Architecture Type:** Autoencoder + Flow model.\n**Network Architecture:** Transformer.\n Proteina-Complexa uses three neural networks, an encoder, a decoder, and a denoiser, all of which share a core non-equivariant transformer architecture with pair-biased attention mechanisms. For refining the pair representation, optional triangle multiplicative layers can be included within the denoiser network. The architecture operates on a partially latent representation, explicitly modeling the protein's three-dimensional alpha-carbon coordinates while capturing the sequence and all other atomistic details in per-residue eight-dimensional latent variables. The denoiser network parametrizes the flow that maps a noise distribution to the joint distribution of alpha-carbon coordinates and latent variables, which are iteratively updated during the generation process. The denoiser also takes as inputs three-dimensional target structure and sequence for protein or small molecule targets that are then used as conditioning for the design of the binding protein. The decoder then generates the final fully atomistic binder structure from these outputs.\n\n\n**This model was developed based on:** La-Proteina (https://github.com/NVIDIA-Digital-Bio/la-proteina).
\n**Number of model parameters:** 1.6*10^8
\n\n\n\n## Input:
\n**Input Type(s):** \n- Text (time step schedules, noise schedules, sampling modes, motif coordinates, target coordinates)
\n- Number (number of residues, noise scales, time step sizes, seed, noise schedule exponents)
\n- Binary (use of self-conditioning)
\n\n**Input Format:** \n- Text: Strings (time step schedules, noise schedules, sampling modes), PDB file (motif coordinates)
\n- Number: Integers (number of residues, seed), floats (noise scales, time step sizes, noise schedule exponents)
\n- Binary: Booleans
\n\n**Input Parameters:** \n- Text: One-dimensional (1D) or text file (PDB file)
\n- Number: One-dimensional (1D)
\n- Binary: One-dimensional (1D)
\n\n\n## Output:
\n**Output Type(s):** Text (generated atomistic coordinates and sequence)
\n**Output Format:** Text: PDB file (generated protein with sequence and all atom coordinates)
\n**Output Parameters**: One-dimensional (1D)
\n**Other Properties Related to Output:** The model output is stored as a PDB file containing the protein sequence and three-dimensional coordinates for all atoms.
\n\nOur 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.
\n\n## Software Integration: \n**Runtime Engine(s):** \n* [Pytorch]
\n**Supported Hardware Microarchitecture Compatibility:**
\n* [NVIDIA Ampere]
\n\n**Preferred/Supported Operating System(s):**\n* [Linux]
\n\n\nThe 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.
\n\n\n## Model Version(s):\nWe release ten model checkpoints, seven for latent diffusion models and three for the corresponding autoencoders.\nLatent diffusion checkpoints (used together with the corresponding autoencoder below):\n- NV-Proteina-Complexa-Protein-Target-160M-v1\n- NV-Proteina-Complexa-Ligand-Target-160M-v1\n- NV-Proteina-Complexa-AME-160M-v1\n
\n\n\n## Training, Testing, and Evaluation Datasets:\n\n### Dataset Overview:\n**Total Size:** Approximately 1,300,000 data points \n**Total Number of Datasets:** 4 (AFDB, Teddymer, PDB and PLINDER)\n**Dataset partition:** Training 99.9%, Validation 0.1% \n**Time period for training data collection:** The AFDB was generated using Alpha Fold 2, published in 2021, the PDB consists of experimental data, which started being collected in 1971.\n**Time period for testing data collection:** See above.\n**Time period for validation data collection:** See above.\n\n\n## Training Dataset:\n(1) **AFDB Link:** https://alphafold.ebi.ac.uk/ \n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nSynthetic (AlphaFold predictions)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The AlphaFold Protein Structure Database (AFDB) contains approximately 214M synthetic three-dimensional protein structures predicted by AlphaFold2, along with their corresponding sequences. We trained Complexa models on a subset of the AFDB, one comprising 344.508 structures.\n\n(2) **Teddymer:** \n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nSynthetic (AlphaFold predictions)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The Teddymer dataset contains around 500,000 synthetic dimer pairs created by annotating, filtering and cropping structure predictions from the AFDB.\n\n(3) **PDB Link:** https://www.rcsb.org/\n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nNot Applicable: Experimental structure determination (X-ray crystallography, cryo-EM, NMR)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** We use a filtered subset of the PDB during model training.\n\n(4) ** PLINDER Link:** https://console.cloud.google.com/storage/browser/plinder\n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nNot Applicable: Experimental structure determination (X-ray crystallography, cryo-EM, NMR)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** We use a filtered subset of PLINDER (around 80,000 structures) as training set for our ligand models.\n\n### Testing Dataset:\n(1) AFDB\n**Link:** https://alphafold.ebi.ac.uk/ \n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nSynthetic (AlphaFold predictions)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** We use a subset of the AFDB subset of 734,658 structures as a reference set in evaluations. We also extract a subset of files, and modify them to create the benchmark for binder design. We release these modified files with the codebase.\n\n(2) PDB\n**Link:** https://www.rcsb.org/\n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nSynthetic (AlphaFold predictions)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** We use the entire PDB as reference set in evaluations. We also extract a subset of files, and modify them to create the benchmark for binder design. We release these modified files with the codebase.\n\n### Evaluation Dataset:\n**Link:** https://alphafold.ebi.ac.uk/ \n\n**Data Modality:**\n* Text (PDB files) \n \n**Non-Audio, Image, Text Training Data Size:** \n* Between 0.5MB and 2MB per sample (PDB file)\n\n**Data Collection Method by dataset:** \nSynthetic (AlphaFold predictions)\n\n**Labeling Method by dataset:** N/A\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** We use a subset of the AFDB subset of 1,300 structures as a validation set during training.\n\n**Quantitative Evaluation Benchmarks**\n\n#### Small molecule targets:\nQuantitative evaluation of generative performance on four small-molecule binders (SAM, OQO, FAD, IAI) without test-time optimization.
\n\n| Model | Time (s) | Novelty | SAM unique successes | OQO unique successes | FAD unique successes | IAI unique successes |\n|---------------------|---------:|--------:|----------------------:|----------------------:|----------------------:|----------------------:|\n| RFDiffusion-AllAtom | 2 | 0.72 | 2 | 3 | 5 | 8 |\n| Proteina-Complexa (ours) | 10 | 0.71 | 10 | 6 | 17 | 19 |\n\n#### Protein targets\nQuantitative evaluation of generative performance on protein binder targets without test-time optimization, comparing Complexa to prior generative baselines.
\n\n| Model | Time (s) | Novelty | Self unique successes | MPNN-FI unique successes | MPNN unique successes |\n|---------------------|---------:|--------:|----------------------:|--------------------------:|----------------------:|\n| RFDiffusion | 4.68 | 0.87 | 3 | 70.8 | – |\n| Protpardelle-1c | 0.73 | 0.77 | 0 | 8.13 | – |\n| APM | 0.31 | 0.86 | 1 | 73.1 | 3.15 |\n| Proteina-Complexa (ours) | 9.10 | 0.80 | 14 | 15.6 | 14.4 |\n\n\n#### Inference-time optimization\n\nQuantitative evaluation of beam-search–based test-time optimization with different folding and hydrogen-bond rewards.
\n\n| Configuration | Unique successes (avg.) | Interface H-bonds (avg.) |\n|-----------------------------------|------------------------:|--------------------------:|\n| Proteina-Complexa, no reward | 77.00 | 5.271 |\n| Proteina-Complexa, with f_ipAE | 83.36 | 5.524 |\n| Proteina-Complexa, with f_H-Bond | 82.36 | 7.154 |\n| Proteina-Complexa, with f_ipAE + f_H-Bond | 86.26 | 6.518 |\n\n\nFor more information: [Here](https://openreview.net/forum?id=qmCpJtFZra)

\n\n# Inference:\n**Acceleration Engine:** Pytorch
\n**Test Hardware:** A100
\n**Test Hardware:** NVIDIA A100
\n\n## Ethical Considerations:\nNVIDIA 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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\n\nFor more detailed information on ethical considerations for this model, please see the [Model Card++ Bias](bias.md), [Explainability](explainability.md), [Safety & Security](safety.md), and [Privacy](privacy.md)
\n\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.
\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
","displayName","Proteina Complexa Ligand","updatedDate",["D",1773594496584],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/models/proteina_complexa_ligand","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",5890739041,"licenseTerms",{"_212":65,"_107":14,"_108":109,"_213":18},"isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Not Applicable\nBias Metric (If Measured): | Not Applicable\nMeasures taken to mitigate against unwanted bias: | Not Applicable","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Structural biology\nModel Type: | Transformer\nIntended Users: | Protein Designers, Structural Biologists, Bioengineers, Computational Biologists, Protein Engineers.\nOutput: | Types: Text (generated atomistic coordinates and sequence). Formats: Text: PDB file (generated protein with sequence and all atom coordinates)\nDescribe how the model works: | Complexa uses three neural networks: an encoder, a decoder, and a denoiser, all of which share a core non-equivariant transformer architecture with pair-biased attention mechanisms. For refining the pair representation, optional triangle multiplicative layers can be included within the denoiser network. The architecture operates on a partially latent representation, explicitly modeling the protein's three-dimensional alpha-carbon coordinates while capturing the sequence and all other atomistic details in per-residue eight-dimensional latent variables. The denoiser network parameterizes the flow that maps a noise distribution to the joint distribution of alpha-carbon coordinates and latent variables, which are iteratively updated during the generation process. The decoder then generates the final fully atomistic structure from these outputs. Parameters: 1.6*10^8\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | The primary technical limitation of the Complexa model is its focus on fixed target structures. The current version of the model has not been applied to binder design with flexible targets or to DNA/RNA targets, which is a critical task for many biological applications such as designing de novo binders and enzymes. \nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Co-designability, Diversity, Novelty, Binder Success Metrics like ipAE (interface predicted aligned error) /min ipAE scores.\nPotential Known Risks: | When the model does not work as intended, it may produce proteins with poor co-designability (an incompatible sequence and structure) or fail on conditional tasks like binder design by not accurately designing a functional interface. All generated outputs should be treated as proposals that require screening and validation with standard structural assessment tools before any experimental use. \nLicensing: | GOVERNING TERMS: Use of the model is governed by the Governing Terms: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). The model will be released on GitHub on March 15 under the Apache 2.0 open-source license.","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | No\nHow often is dataset reviewed? | Before Release\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nWas data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data\nApplicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)","safetyAndSecurity","# Safety & Security Subcard\nField | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Structural Biology\nDescribe the life critical impact (if present). | Experimental drug discovery and medicine: Additional in silico and in vitro tests are recommended before using the proteins for downstream applications.\nUse Case Restrictions: | Use of this model is governed by the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). The model will be released on GitHub on March 15 under the Apache 2.0 open-source license.\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[178,179,180,181,182,183,184],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_139":209,"_186":210,"_188":211,"_190":191},{"_139":205,"_186":206,"_188":207,"_190":191},{"_139":203,"_186":203,"_188":204,"_190":191},{"_139":200,"_186":201,"_188":202,"_190":191},{"_139":196,"_186":197,"_188":198,"_190":191},{"_139":194,"_186":194,"_188":195,"_190":191},{"_139":185,"_186":187,"_188":189,"_190":191},"NVIDIA AI","value","soln_nvidia_ai","filter",{"_192":193,"_186":187},"category","label","field","technology","NSPECT-X5VA-YFLY",{"_192":191,"_186":194},"Life Sciences","indus_life_sciences",{"_192":199,"_186":197},"industry","Healthcare","indus_healthcare",{"_192":199,"_186":201},"False",{"_192":191,"_186":203},"Clara","pltfm_clara",{"_192":208,"_186":206},"platform","AI","soln_ai",{"_192":193,"_186":210},"governingTerms","needsAcceptance","appConfig",{"_1393":1394,"_248":1395},"ngcApps",[1346],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1328":1329,"_1330":1331,"_1332":1333,"_1334":1335,"_1336":226,"_79":253,"_1337":1338,"_1326":48,"_1339":18,"_1340":18,"_1341":1342,"_1343":18,"_1344":1345},"amplitude",{"_1324":1325,"_1326":48,"_926":-7,"_1327":16},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1318":1319,"_1320":1321,"_1322":1323},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1201":151,"_1202":1203,"_1204":1205,"_1206":1207},"announcements",[],"consentState",{"_292":1191,"_1192":18,"_1193":16,"_1194":16,"_1195":1196},"consentConfig",{"_274":275,"_276":277,"_278":279,"_280":281,"_282":283,"_284":285,"_286":287,"_288":289,"_290":291,"_292":293},"showContactAdmin",1783067237855,[],[],[267,268,269,270,271,272],{"_37":52,"_39":62,"_41":273,"_43":-5,"_45":-5,"_47":48},{"_37":52,"_39":60,"_41":273,"_43":-5,"_45":-5,"_47":48},{"_37":52,"_39":57,"_41":273,"_43":-5,"_45":-5,"_47":48},{"_37":52,"_39":53,"_41":273,"_43":-5,"_45":-5,"_47":48},{"_37":49,"_39":50,"_41":42,"_43":44,"_45":51,"_47":48},{"_37":38,"_39":40,"_41":273,"_43":-5,"_45":-5,"_47":48},"pending","mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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