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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the Above
Measures taken to mitigate against unwanted bias:	None of the Above

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OpenFold

Model Overview

Description:

This is an OpenFold implementation under BioNeMo framework, derived from public OpenFold and DeepMind AlphaFold-2. This checkpoint was fine-tuned on the initial training checkpoint from the public OpenFold team. OpenFold predicts protein structures from protein sequence inputs and optional multiple sequence alignments (MSAs) and template(s). This implementation supports initial training, fine-tuning and inference under BioNeMo framework. Detailed examples can be found under examples/protein/openfold within BioNeMo framework repository.

Users are advised to read the licensing terms under public OpenFold and DeepMind AlphaFold-2 repositories as well as our copyright text.

This model is ready for commercial use.

References:

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization | Link
Highly accurate protein structure prediction with AlphaFold | Link
OpenProteinSet: Training data for structural biology at scale | Link

Model Architecture:

Architecture Type: Pose Estimation
\nNetwork Architecture: AlphaFold-2

Input:

Input Type(s): Protein Sequence, (optional) Multiple Sequence Alignment(s) and (optional) Strutural Template(s)
\nInput Format(s): None, a3m (text file), hhr (text file)
\nInput Parameters: 1D
\nOther Properties Related to Input: None

Output:

Output Type(s): Protrin Structure Pose(s), (optional) Confidence Metrics, (optional) Embeddings
\nOutput Format: PDB (text file), Pickle file, Pickle file
\nOutput Parameters: 3D
\nOther Properties Related to Output: Pose (num_atm_ x 3), (optional) Confidence Metric: pLDDT (num_res_) and PAE (num_res_ x num_res_), (optional) Embeddings (num_res_ x emb_dims, or num_res_ x num_res_ x emb_dims)

Software Integration:

Runtime Engine(s):

NeMo, BioNeMo

Supported Hardware Microarchitecture Compatibility:

[Ampere]
[Hopper]

[Preferred/Supported] Operating System(s):

[Linux]

Model Version(s):

OpenFold under BioNeMo framework

Training & Evaluation:

Training Dataset:

Link: PDB-mmCIF dataset, OpenProteinSet
\nData Collection Method by dataset

PDB-mmCIF dataset: [Automatic] and [Human]
OpenProteinSet: [Automatic]

Labeling Method by dataset

[Not Applicable]

Properties: PDB-mmCIF dataset: 200k samples of experimental protein structures. OpenProteinSet: 269k samples on sequence alignments.
\nDataset License(s): PDB-mmCIF dataset: CC0 1.0 Universal. OpenProteinSet: CC BY 4.0.

Evaluation Dataset:

Link: PDB-mmCIF dataset, OpenProteinSet
\nData Collection Method by dataset

PDB-mmCIF dataset: [Automatic] and [Human]
OpenProteinSet: [Automatic]

Labeling Method by dataset

[Not Applicable]

Inference:

Engine: NeMo, BioNeMo, Triton
\nTest Hardware:

[Ampere]
[Hopper]

Benchmarks

Accuracy benchmark

There are two stages of training OpenFold: initial-training and fine-tuning. 4 checkpoints are available for download: a pair of initial-training and fintuining available publicly and converted to .nemo format, and another pair of in-house trained checkpoints. All checkpoints are benchmarked against CAMEO benchmark with proteins dated from 2021-09-17 to 2021-12-11. This validation set is available through training data.

Benchmark results (lDDT-cα) for checkpoints trained using BioNeMo framework:

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

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

	initial-training	fine-tuning*
CAMEO 2021-09-17 to 2021-12-11	89.82	91.0

*this checkpoint was fine-tuned starting from public inital-training, and is available for download via NGC (using the download_models.py script).

Training Performance Benchmarks

Training speed was tested 16 DGX-A100 (128 GPUs) with 80GB of memory, with a single protein (micro batch size of 1) per GPU.

\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\">

	initial-training	fine-tuning
number of steps	80,000	12,000
training step time (s)	6.06	24.91

Note that in the default configuration of OpenFold training shipped in BioNeMo, there is validation every 200 steps, which takes about 3 minutes of duration. Initial training therefore takes approximately 6.5 days.

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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### References:\n\n1. OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization | [Link](https://www.biorxiv.org/content/10.1101/2022.11.20.517210)\n2. Highly accurate protein structure prediction with AlphaFold | [Link](https://pubmed.ncbi.nlm.nih.gov/34265844/)\n3. OpenProteinSet: Training data for structural biology at scale | [Link](https://arxiv.org/abs/2308.05326)\n\n### Model Architecture:\n\n**Architecture Type:** Pose Estimation
\n**Network Architecture:** AlphaFold-2
\n\n### Input:\n\n**Input Type(s):** Protein Sequence, (optional) Multiple Sequence Alignment(s) and (optional) Strutural Template(s)
\n**Input Format(s):** None, a3m (text file), hhr (text file)
\n**Input Parameters:** 1D
\n**Other Properties Related to Input:** None
\n\n### Output:\n\n**Output Type(s):** Protrin Structure Pose(s), (optional) Confidence Metrics, (optional) Embeddings
\n**Output Format:** PDB (text file), Pickle file, Pickle file
\n**Output Parameters:** 3D
\n**Other Properties Related to Output:** Pose (num_atm_ x 3), (optional) Confidence Metric: pLDDT (num_res_) and PAE (num_res_ x num_res_), (optional) Embeddings (num_res_ x emb_dims, or num_res_ x num_res_ x emb_dims)
\n\n### Software Integration:\n\n**Runtime Engine(s):**\n* NeMo, BioNeMo
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* [Ampere]
\n* [Hopper]
\n\n**[Preferred/Supported] Operating System(s):**
\n* [Linux]\n\n### Model Version(s):\n\nOpenFold under BioNeMo framework
\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n**Link:** [PDB-mmCIF dataset](https://www.rcsb.org), [OpenProteinSet](https://arxiv.org/abs/2308.05326)
\n**Data Collection Method by dataset**
\n* PDB-mmCIF dataset: [Automatic] and [Human]
\n* OpenProteinSet: [Automatic]
\n\n**Labeling Method by dataset**
\n* [Not Applicable]
\n\n**Properties:** PDB-mmCIF dataset: 200k samples of experimental protein structures. OpenProteinSet: 269k samples on sequence alignments.
\n**Dataset License(s):** PDB-mmCIF dataset: [CC0 1.0 Universal](https://www.rcsb.org/pages/usage-policy). OpenProteinSet: [CC BY 4.0](https://registry.opendata.aws/openfold/).\n\n### Evaluation Dataset:\n\n**Link:** [PDB-mmCIF dataset](https://www.rcsb.org), [OpenProteinSet](https://arxiv.org/abs/2308.05326)
\n**Data Collection Method by dataset**
\n* PDB-mmCIF dataset: [Automatic] and [Human]
\n* OpenProteinSet: [Automatic]
\n\n**Labeling Method by dataset**
\n* [Not Applicable]
\n\n**Properties:** PDB-mmCIF dataset: 200k samples of experimental protein structures. OpenProteinSet: 269k samples on sequence alignments.
\n**Dataset License(s):** PDB-mmCIF dataset: [CC0 1.0 Universal](https://www.rcsb.org/pages/usage-policy). OpenProteinSet: [CC BY 4.0](https://registry.opendata.aws/openfold/).\n\n### Inference:\n\n**Engine:** NeMo, BioNeMo, Triton
\n**Test Hardware:**
\n* [Ampere]
\n* [Hopper]
\n\n### Benchmarks\n\n#### Accuracy benchmark\n\nThere are two stages of training OpenFold: initial-training and fine-tuning. 4 checkpoints are available for download: a pair of initial-training and fintuining available publicly and converted to .nemo format, and another pair of in-house trained checkpoints. All checkpoints are benchmarked against [CAMEO benchmark](https://cameo3d.org/modeling/3-months/) with proteins dated from 2021-09-17 to 2021-12-11. This validation set is available through training data.\n\nBenchmark results (lDDT-cα) for checkpoints trained using BioNeMo framework:\n\n| | initial-training | fine-tuning* |\n|---------------------------------|------------------|--------------|\n| CAMEO 2021-09-17 to 2021-12-11 | 89.82 | 91.0 |\n\n*this checkpoint was fine-tuned starting from **public** inital-training, and is available for download via NGC (using the `download_models.py` script).\n\n#### Training Performance Benchmarks\n\nTraining speed was tested 16 DGX-A100 (128 GPUs) with 80GB of memory, with a single protein (micro batch size of 1) per GPU.\n\n| | initial-training | fine-tuning |\n|------------------------|------------------|-------------|\n| number of steps | 80,000 | 12,000 |\n| training step time (s) | 6.06 | 24.91 |\n\nNote that in the default configuration of OpenFold training shipped in BioNeMo, there is validation every 200 steps, which takes about 3 minutes of duration. Initial training therefore takes approximately 6.5 days.\n\n### Ethical Considerations:\n\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 supporting 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++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","OpenFold","updatedDate",["D",1731434396698],"publisher","NVIDIA","latestVersion","finetuned_1.2","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/models/openfold","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes",346695712,"licenseTerms","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: | None of the Above\nMeasures taken to mitigate against unwanted bias: | None of the Above","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------\nIntended Application(s) & Domain(s): | Structural Biology for Drug Discovery\nModel Type: | Pose Estimation\nIntended Users: | This model is intended for bioscience researchers to infer protein structures from sequences for early-stage drug discovery and protein-related industrial processes.\nOutputs: | Geometric Protein Structure (Text), Confidence Score (Optional), Embeddings (Optional)\nDescribe how the model works: | Predicts the pose of input protein sequence(s) from protein sequence alignments.\nTechnical Limitations: | This model does not work for nucleic acids and other small molecules. This model is less capable in predicting proteins that lack multiple sequence alignments (MSAs), such as complementarity-determining regions (CDR) loops in antibody and de novo proteins. \nVerified to have met prescribed NVIDIA standards: | Yes\nPerformance Metrics: | 1. Coordinate deviation between experimental protein structure and prediction
2. Recovery of masked amino acid identity on protein sequence
3. Deviation between experiment and prediction distogram between pairs of amino acids
4. Uncertainty estimation by predicted Local Distance Difference Test (LDDT)\nPotential Known Risks: | Model may inaccurately model biomolecular systems in drug development and bioengineering.\nLicensing: | https://developer.download.nvidia.com/licenses/NVIDIA-BioNeMo-Framework-Evaluation-Software%20License(14Nov2023).pdf","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nProtected classes used to create this model? | None\nWas consent obtained for any PII used? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Not Applicable\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 NVIDIA Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:------------------------------------------------------------------\nModel Application(s): | Protein Structure Prediction\nDescribe the life-critical application (if present).| Should not be used for life-critical use cases per https://developer.download.nvidia.com/licenses/NVIDIA-BioNeMo-Framework-Evaluation-Software%20License(14Nov2023).pdf\nUse Case Restrictions: | Abide by https://developer.download.nvidia.com/licenses/NVIDIA-BioNeMo-Framework-Evaluation-Software%20License(14Nov2023).pdf\nDescribe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452],[290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306],{"_307":414,"_118":414,"_116":309,"_280":415},{"_307":409,"_118":409,"_116":309,"_280":410},{"_307":405,"_118":405,"_116":309,"_280":406},{"_307":400,"_118":400,"_116":309,"_280":401},{"_307":395,"_118":395,"_116":309,"_280":396},{"_307":383,"_118":383,"_116":309,"_280":384},{"_307":371,"_118":371,"_116":309,"_280":372},{"_307":366,"_118":366,"_116":309,"_280":367},{"_307":355,"_118":355,"_116":309,"_280":356},{"_307":350,"_118":350,"_116":309,"_280":351},{"_307":345,"_118":345,"_116":309,"_280":346},{"_307":340,"_118":340,"_116":309,"_280":341},{"_307":335,"_118":335,"_116":309,"_280":336},{"_307":330,"_118":330,"_116":309,"_280":331},{"_307":325,"_118":325,"_116":309,"_280":326},{"_307":318,"_118":318,"_116":309,"_280":319},{"_307":308,"_118":308,"_116":309,"_280":310},"hostName","amazon-adsystem.com","",[311,312],{"_274":317,"_314":308,"_315":20,"_116":316},{"_274":313,"_314":308,"_315":20,"_116":316},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[320,321],{"_274":324,"_314":318,"_315":20,"_116":323},{"_274":322,"_314":318,"_315":20,"_116":323},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[347],{"_274":348,"_314":345,"_315":20,"_116":349},"sp","Testing","google.com",[352],{"_274":353,"_314":350,"_315":20,"_116":354},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. 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It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[373,374,375,376],{"_274":382,"_314":371,"_315":20,"_116":378},{"_274":380,"_314":371,"_315":20,"_116":381},{"_274":379,"_314":371,"_315":20,"_116":378},{"_274":377,"_314":371,"_315":20,"_116":378},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[385,386,387,388],{"_274":394,"_314":383,"_315":20,"_116":392},{"_274":393,"_314":383,"_315":20,"_116":392},{"_274":391,"_314":383,"_315":20,"_116":392},{"_274":389,"_314":383,"_315":20,"_116":390},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[397],{"_274":398,"_314":395,"_315":20,"_116":399},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[402],{"_274":403,"_314":400,"_315":20,"_116":404},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[407],{"_274":408,"_314":405,"_315":20,"_116":349},"R","6sc.co",[411],{"_274":412,"_314":409,"_315":20,"_116":413},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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. If you do not allow these cookies, then some or all of these services may not function properly.",4,[542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562],[507,508,509,510,511],{"_307":400,"_118":400,"_116":309,"_280":533},{"_307":366,"_118":366,"_116":309,"_280":528},{"_307":521,"_118":521,"_116":309,"_280":522},{"_307":517,"_118":517,"_116":309,"_280":518},{"_307":512,"_118":512,"_116":309,"_280":513},"nvcrm.my.site.com",[514],{"_274":515,"_314":512,"_315":20,"_116":516},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[519],{"_274":520,"_314":517,"_315":20,"_116":349},"edge","github.com",[523,524],{"_274":527,"_314":521,"_315":20,"_116":526},{"_274":525,"_314":521,"_315":20,"_116":526},"logged_in","This domain is owned by Github. 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