Model Overview

Description:

RNAPro is a model for predicting RNA 3D structure from sequence, combining AF3-like co-folding architectures with RNA foundation models, MSAs, and template-based modeling. It tackles the challenge of accurate RNA structure prediction by providing a computational alternative to expensive, time-consuming wet-lab structure determination, thereby supporting structure-driven drug discovery. The primary users are computational biologists, drug discovery researchers, and developers working on representation learning and generative AI for biological data. The model integrates into drug discovery pipelines by reducing wet-lab burden for target identification, validation, and downstream RNA generative modeling. This model is ready for commercial and non-commercial use.

License/Terms of Use

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

Deployment Geography:

Global

Use Case:

RNAPro can be used by RNA therapeutics developers for understanding RNA function and designing RNA-based therapeutics.

Release Date:

GitHub 01/09/2026 via https://github.com/NVIDIA-Digital-Bio/RNAPro
\nHugging Face 01/09/2026 via:

NGC 01/09/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/

Model Architecture:

RNAPro consists of an input embedder, an MSA module, a gating module, and a template module that process the input sequence, MSA, RNA foundation model features, and templates. The Pairformer block is used to update the single and pair representations. Finally, a diffusion module takes these updated single and pair representations to predict the 3D structure.

Number of model parameters: 488,301,921\n

Input:

Input Type(s): Text (RNA sequence, MSA), Binary (templates)
\nInput Format:

Text: CSV (RNA sequence), FASTA (MSA)
Binary: Templates

Input Parameters:

Text: 1D
Binary: 3D

Other Properties Related to Input: RNA sequence, MSA, and templates are automatically cropped to the 512 length.

Output:

Output Type(s): RNA 3D structure coordinates
\nOutput Format: CIF
\nOutput Parameters: 3D
\nOther Properties Related to Output: CIF files including all atom structures will be saved.

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

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Blackwell

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 are releasing three best models — one each for the private, public, and RNA-puzzle target datasets — based on the leaderboard test datasets from the Stanford RNA 3D Folding Kaggle Competition.\n

RNAPro-Private-Best-500M
RNAPro-Public-Best-500M

Training, Testing, and Evaluation Datasets:

Training Datasets:

(1) Stanford RNA 3D Folding Kaggle dataset Link: Stanford RNA 3D Folding\n

Data Modality

Text (RNA sequence, MSA, structures)

Properties: The Stanford RNA 3D Folding dataset contains 5,135 RNA sequences and structure labels. MSA files are included for some RNA sequences. Training labels include only alpha-carbon structures.

Non-Audio, Image, Text Training Data Size: The dataset contains corresponding CSVs, FASTAs, and CIFs totaling 65.1GB.

Data Collection Method:

Human

Labeling Method by Dataset:

Human

(2) Stanford RNA 3D Folding all-atom Link: Stanford RNA 3D Folding all atom\n

Data Modality

Text (RNA sequence, MSA, structures)

Properties: Stanford RNA 3D Folding all atom dataset contains 5,135 RNA sequences and structures. Training labels include all-atom structures.

Non-Audio, Image, Text Training Data Size: The dataset contains corresponding CSVs, totaling 108.49 GB.

Data Collection Method:

Human

Labeling Method by Dataset:

Human

(3) Protenix dataset Link: Protenix dataset\n

Data Modality

Text (biomolecular CIF files, JSON files, MSA files)

Properties: This dataset includes biological sequences, molecules, and structure files to train a biomolecule structure prediction model called Protenix. We used the files components.v20240608.cif and components.v20240608.cif.rdkit_mol.pkl for this project.

Non-Audio, Image, Text Training Data Size: The dataset contains pre-processed files, MSAs, structure files, totaling 1TB.

Data Collection Method:

Human

Labeling Method by Dataset:

Human

Evaluation Datasets:

Stanford RNA 3D Folding private dataset Link: Stanford RNA 3D Folding\n

Data Modality

Text (RNA sequence, MSA, structures)

Properties: The Stanford RNA 3D Folding private dataset contains recently synthesized RNA sequences and structure labels.

Non-Audio, Image, Text Training Data Size: The dataset contains corresponding CSVs and CIFs.

Data Collection Method:

Human

Labeling Method by Dataset:

Human

Inference:

Acceleration Engine: cuEquivariance
\nTest Hardware: A100, H100, GB300

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 Subcards.

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.

","resourceVersions",[230,231],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_213":28,"_214":-5,"_52":117,"_114":-5,"_118":215,"_120":-5,"_216":116,"_125":126,"_122":91,"_217":218,"_121":91,"_219":-5,"_220":34,"_221":130,"_222":34,"_223":130,"_224":34,"_225":130,"_226":34,"_227":130,"_228":34,"_123":229},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/clara/resources/rnapro/-","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/clara/rnapro:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara/resources/rnapro/-/version-history","accessCheckResult",{"_212":91},"hasRelatedCollections","context",{"_208":209,"_210":211},1784178445963,[],[],[201,202,203,204,205,206,207],{"_48":63,"_50":74,"_52":65,"_54":66,"_56":75,"_58":59},{"_48":63,"_50":72,"_52":149,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","clara","allowed","versionId","description",["D",1767986132355],"totalSizeInBytes","malwareScanStatus","CLEAN","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_86":185,"_113":233,"_114":-5,"_122":91,"_121":91,"_123":232,"_220":34},{"_86":28,"_113":28,"_114":-5,"_122":91,"_121":91,"_123":232,"_220":34},[],"Latest (1.0)","nvidia/clara/rnapro","key","nvidia/clara/resources/rnapro","resourceId","rnapro","shortDescription","RNAPro Model Weights","# Model Overview\n\n## Description:\nRNAPro is a model for predicting RNA 3D structure from sequence, combining AF3-like co-folding architectures with RNA foundation models, MSAs, and template-based modeling. It tackles the challenge of accurate RNA structure prediction by providing a computational alternative to expensive, time-consuming wet-lab structure determination, thereby supporting structure-driven drug discovery. The primary users are computational biologists, drug discovery researchers, and developers working on representation learning and generative AI for biological data. The model integrates into drug discovery pipelines by reducing wet-lab burden for target identification, validation, and downstream RNA generative modeling. This model is ready for commercial and non-commercial use.
\n\n\n\n## License/Terms of Use\n\nGoverning Terms: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Deployment Geography:\nGlobal
\n\n## Use Case:
\n\n- RNAPro can be used by RNA therapeutics developers for understanding RNA function and designing RNA-based therapeutics.
\n\n## Release Date:
\nGitHub 01/09/2026 via https://github.com/NVIDIA-Digital-Bio/RNAPro
\nHugging Face 01/09/2026 via:\n- https://huggingface.co/nvidia/RNAPro-Private-Best-500M\n- https://huggingface.co/nvidia/RNAPro-Public-Best-500M\n
\n\nNGC 01/09/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/
\n\n\n\n## Model Architecture:\nRNAPro consists of an input embedder, an MSA module, a gating module, and a template module that process the input sequence, MSA, RNA foundation model features, and templates. The Pairformer block is used to update the single and pair representations. Finally, a diffusion module takes these updated single and pair representations to predict the 3D structure.\n\nNumber of model parameters: 488,301,921\n
\n\n## Input:
\n**Input Type(s):** Text (RNA sequence, MSA), Binary (templates)
\n**Input Format:** \n- Text: CSV (RNA sequence), FASTA (MSA)\n- Binary: Templates\n\n**Input Parameters:**
\n- Text: 1D \n- Binary: 3D\n\n**Other Properties Related to Input:** RNA sequence, MSA, and templates are automatically cropped to the 512 length.\n\n\n## Output:
\n**Output Type(s):** RNA 3D structure coordinates
\n**Output Format:** CIF
\n**Output Parameters:** 3D
\n**Other Properties Related to Output:** CIF files including all atom structures will be saved.\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\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper\n* NVIDIA Blackwell\n\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 are releasing three best models — one each for the private, public, and RNA-puzzle target datasets — based on the leaderboard test datasets from the [Stanford RNA 3D Folding Kaggle Competition.\n](https://www.kaggle.com/competitions/stanford-rna-3d-folding)\n\n- RNAPro-Private-Best-500M
\n- RNAPro-Public-Best-500M
\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Datasets:\n\n(1) Stanford RNA 3D Folding Kaggle dataset **Link:** [Stanford RNA 3D Folding\n](https://www.kaggle.com/competitions/stanford-rna-3d-folding)
\n\n**Data Modality**
\n* Text (RNA sequence, MSA, structures)
\n\n**Properties:** The Stanford RNA 3D Folding dataset contains 5,135 RNA sequences and structure labels. MSA files are included for some RNA sequences. Training labels include only alpha-carbon structures.
\n\n**Non-Audio, Image, Text Training Data Size:** The dataset contains corresponding CSVs, FASTAs, and CIFs totaling 65.1GB.\n\n**Data Collection Method:**\n* Human
\n\n**Labeling Method by Dataset:**\n* Human
\n\n\n(2) Stanford RNA 3D Folding all-atom **Link:** [Stanford RNA 3D Folding all atom\n](https://www.kaggle.com/datasets/rhijudas/stanford-rna-3d-folding-all-atom-train-data)
\n\n**Data Modality**
\n* Text (RNA sequence, MSA, structures)
\n\n**Properties:** Stanford RNA 3D Folding all atom dataset contains 5,135 RNA sequences and structures. Training labels include all-atom structures.
\n\n**Non-Audio, Image, Text Training Data Size:** The dataset contains corresponding CSVs, totaling 108.49 GB.\n\n**Data Collection Method:**\n* Human
\n\n**Labeling Method by Dataset:**\n* Human
\n\n(3) Protenix dataset **Link:** [Protenix dataset\n](https://github.com/bytedance/Protenix/blob/main/docs/training.md)
\n\n**Data Modality**
\n* Text (biomolecular CIF files, JSON files, MSA files)
\n\n**Properties:** This dataset includes biological sequences, molecules, and structure files to train a biomolecule structure prediction model called Protenix. We used the files `components.v20240608.cif` and `components.v20240608.cif.rdkit_mol.pkl` for this project.
\n\n**Non-Audio, Image, Text Training Data Size:** The dataset contains pre-processed files, MSAs, structure files, totaling 1TB.\n\n**Data Collection Method:**\n* Human
\n\n**Labeling Method by Dataset:**\n* Human
\n\n\n### Evaluation Datasets:\n\nStanford RNA 3D Folding private dataset **Link:** [Stanford RNA 3D Folding\n](https://www.kaggle.com/competitions/stanford-rna-3d-folding)
\n\n**Data Modality**
\n* Text (RNA sequence, MSA, structures)
\n\n**Properties:** The Stanford RNA 3D Folding private dataset contains recently synthesized RNA sequences and structure labels.
\n\n**Non-Audio, Image, Text Training Data Size:** The dataset contains corresponding CSVs and CIFs.\n\n**Data Collection Method:**\n* Human
\n\n**Labeling Method by Dataset:**\n* Human
\n\n## Inference:\n**Acceleration Engine:** cuEquivariance
\n**Test Hardware:** A100, H100, GB300
\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, Explainability, Safety & Security, and Privacy Subcards.\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/).
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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. 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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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