Model Overview

Description

ReaSyn is a model for predicting the synthesis pathway, reaction steps from reactants to finadatal product(s), for a target product molecule. When the target molecule cannot be synthesized directly using known reaction steps, ReaSyn will generate the pathways for the most structurally similar, synthesizable analog of the target molecule.The model uses an encoder-decoder transformer architecture and a chain-of-reaction notation, where a full synthetic pathway is represented as a text sequence. This approach allows the model to achieve SOTA performance in tasks like synthesis planning and incorporating synthesizability into goal-directed molecular property optimization.

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. ReaSyn source code is licensed under Apache 2.0.

Deployment Geography: Global

Use Case:
\nReaSyn is a model for predicting the synthetic pathway, reaction steps from reactants to final product(s), for a target product molecule. The model can be used in the pharmaceutical and chemical industries and in academic research to identify how to synthesize a molecule, help chemists in planning a first time synthesis of a molecule, the optimization of an existing synthesis pathway, or the filtering of candidate molecules based on ease of synthesis.

Release Date:
\nGithub 09/23/2025 via https://github.com/NVIDIA-Digital-Bio/ReaSyn
\nNGC 09/23/2025

References

Research paper: “Rethinking Molecule Synthesizability with Chain-of-Reaction”

Model Architecture

Architecture Type: Encoder-decoder\nNetwork Architecture: Encoder-decoder Transformer\nReaSyn utilizes an Encoder-decoder Transformer architecture which takes a molecular SMILES as input and outputs its synthetic pathway. Encoder contains 6 layers and decoder contains 10 layers. Both encoder and decoder have a hidden size of 768, 16 attention heads, and a feed-forward dimension of 4096.

The total number of parameters in ReaSyn is 166M.

Input

Input Types: Text
\nInput Formats: SMILES string
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: Maximum input length is 256 tokens.

Output

Output Types: Text
\nOutput Formats: Chain-of-Reaction sequence (molecular synthetic pathway)
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: Maximum output length is 768 tokens.

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: Torch
\nSupported Hardware Microarchitecture Compatibility: NVIDIA Ampere
\nPreferred Operating System: Linux, Windows

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 Versions

ReaSyn v1

Training and Evaluation Datasets

Training Datasets

SynFormer Reaction Templates
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/rxn_templates/comprehensive.txt
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: Automated
\nProperties: 115 molecular reaction templates in the SMARTS format

Building Blocks in Enamine US Stock retrieved in October 2023
\nLink: https://enamine.net/building-blocks/building-blocks-catalog
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: 115 molecular reaction templates in the SMARTS format

Evaluation Dataset

Enamine REAL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/enamine_smiles_1k.txt
\nhttps://enamine.net/compound-collections/real-compounds/real-database
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from Enamine REAL to evaluate synthesizable molecule reconstruction.

ChEMBL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/chembl_filtered_1k.txt
\nhttps://www.ebi.ac.uk/chembl
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ChEMBL to evaluate synthesizable molecule reconstruction.

ZINC250k Test Set
\nLink: https://www.kaggle.com/datasets/basu369victor/zinc250k
\nData Collection Method by dataset: Synthetic
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ZINC250k to evaluate synthesizable molecule reconstruction.

Inference

Engine: Torch
\nTest Hardware: Ampere / 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.

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

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.

Bias

Participation considerations from adversely impacted groups (protected classes) in model design and testing: None
\nMeasures taken to mitigate against unwanted bias: None

Explainability

Intended Application(s) & Domain(s): Molecular drug discovery and design
\nModel Type: Molecular synthesis pathway generation
\nIntended Users: Developers in the academic or pharmaceutical industries who want to predict synthesis pathways for molecules and who build artificial intelligence applications to perform property guided molecule optimization and novel molecule generation.
\nOutput: Text
\nDescribe how the model works: ReaSyn uses a Transformer encoder-decoder architecture and requires a \"target molecule\" as its input (SMILES format). The model then generates a synthetic pathway for a synthesizable molecule that that molecule, or, if needed, an analog of the input molecule.
\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: Not Applicable
\nTechnical Limitations: The model's reasoning steps, while incorporating reactants, reaction types, and intermediate products, do not account for other important reaction information like environmental conditions or yields. This could be a limitation in real-world drug discovery scenarios.
\nVerified to have met prescribed quality standards?: Yes
\nPerformance Metrics: Reconstruction rate, Similarity, Diversity (Pathway), Diversity (BB).
\nPotential Known Risks: The framework, while effective for generating drug candidates, also has the possibility of generating synthetic pathways for toxic drugs. This requires an additional scheme to be adopted to filter out harmful molecules during the generation search.
\nLicensing & Terms of Use: The use of this model is governed by the NVIDIA Open Model License Agreement.

Privacy

Generatable or reverse engineerable personal data?: No
\nPersonal data used to create this model?: No
\nHow often is the dataset reviewed?: Before Release
\nIs there provenance for all datasets used in training?: Yes
\nDoes data labeling (annotation, metadata) comply with privacy laws?: Yes
\nApplicable Privacy Policy: NVIDIA Privacy Policy

Safety

Model Application(s): Synthesis Pathway Prediction in drug, chemical, and materials design.
\nDescribe life critical impact (if present): Experimental results: Additional in silico and experimental tests are recommended before using the predicted synthesis paths in downstream applications.
\nUse Case Restrictions: Abide by NVIDIA Open Model License Agreement.
\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.

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\nReaSyn is a model for predicting the synthetic pathway, reaction steps from reactants to final product(s), for a target product molecule. The model can be used in the pharmaceutical and chemical industries and in academic research to identify how to synthesize a molecule, help chemists in planning a first time synthesis of a molecule, the optimization of an existing synthesis pathway, or the filtering of candidate molecules based on ease of synthesis.
\n\nRelease Date:
\nGithub 09/23/2025 via https://github.com/NVIDIA-Digital-Bio/ReaSyn
\nNGC 09/23/2025
\n\n### References\nResearch paper: “Rethinking Molecule Synthesizability with Chain-of-Reaction”\n\n### Model Architecture\n\nArchitecture Type: Encoder-decoder\nNetwork Architecture: Encoder-decoder Transformer\nReaSyn utilizes an Encoder-decoder Transformer architecture which takes a molecular SMILES as input and outputs its synthetic pathway. Encoder contains 6 layers and decoder contains 10 layers. Both encoder and decoder have a hidden size of 768, 16 attention heads, and a feed-forward dimension of 4096.\n\nThe total number of parameters in ReaSyn is 166M.\n\n### Input\n\nInput Types: Text
\nInput Formats: SMILES string
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: Maximum input length is 256 tokens.\n\n### Output\n\nOutput Types: Text
\nOutput Formats: Chain-of-Reaction sequence (molecular synthetic pathway)
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: Maximum output length is 768 tokens.\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\n### Software Integration\n\nRuntime Engine: Torch
\nSupported Hardware Microarchitecture Compatibility: NVIDIA Ampere
\nPreferred Operating System: Linux, Windows\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### Model Versions\n\nReaSyn v1\n\n\n## Training and Evaluation Datasets\n\n### Training Datasets\n\nSynFormer Reaction Templates
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/rxn_templates/comprehensive.txt
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: Automated
\nProperties: 115 molecular reaction templates in the SMARTS format\n\nBuilding Blocks in Enamine US Stock retrieved in October 2023
\nLink: https://enamine.net/building-blocks/building-blocks-catalog
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: 115 molecular reaction templates in the SMARTS format\n\n### Evaluation Dataset\n\nEnamine REAL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/enamine_smiles_1k.txt
\nhttps://enamine.net/compound-collections/real-compounds/real-database
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from Enamine REAL to evaluate synthesizable molecule reconstruction.
\n\nChEMBL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/chembl_filtered_1k.txt
\nhttps://www.ebi.ac.uk/chembl
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ChEMBL to evaluate synthesizable molecule reconstruction.
\n\nZINC250k Test Set
\nLink: https://www.kaggle.com/datasets/basu369victor/zinc250k
\nData Collection Method by dataset: Synthetic
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ZINC250k to evaluate synthesizable molecule reconstruction.
\n\n### Inference\n\nEngine: Torch
\nTest Hardware: Ampere / NVIDIA A100\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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\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\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).\n\n\n### Bias\nParticipation considerations from adversely impacted groups (protected classes) in model design and testing: None
\nMeasures taken to mitigate against unwanted bias: None\n\n### Explainability\nIntended Application(s) & Domain(s): Molecular drug discovery and design
\nModel Type: Molecular synthesis pathway generation
\nIntended Users: Developers in the academic or pharmaceutical industries who want to predict synthesis pathways for molecules and who build artificial intelligence applications to perform property guided molecule optimization and novel molecule generation.
\nOutput: Text
\nDescribe how the model works: ReaSyn uses a Transformer encoder-decoder architecture and requires a \"target molecule\" as its input (SMILES format). The model then generates a synthetic pathway for a synthesizable molecule that that molecule, or, if needed, an analog of the input molecule.
\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: Not Applicable
\nTechnical Limitations: The model's reasoning steps, while incorporating reactants, reaction types, and intermediate products, do not account for other important reaction information like environmental conditions or yields. This could be a limitation in real-world drug discovery scenarios.
\nVerified to have met prescribed quality standards?: Yes
\nPerformance Metrics: Reconstruction rate, Similarity, Diversity (Pathway), Diversity (BB).
\nPotential Known Risks: The framework, while effective for generating drug candidates, also has the possibility of generating synthetic pathways for toxic drugs. This requires an additional scheme to be adopted to filter out harmful molecules during the generation search.
\nLicensing & Terms of Use: The 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### Privacy\nGeneratable or reverse engineerable personal data?: No
\nPersonal data used to create this model?: No
\nHow often is the dataset reviewed?: Before Release
\nIs there provenance for all datasets used in training?: Yes
\nDoes data labeling (annotation, metadata) comply with privacy laws?: Yes
\nApplicable Privacy Policy: [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)\n\n### Safety\nModel Application(s): Synthesis Pathway Prediction in drug, chemical, and materials design.
\nDescribe life critical impact (if present): Experimental results: Additional in silico and experimental tests are recommended before using the predicted synthesis paths in downstream applications.
\nUse Case Restrictions: Abide by [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-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.","displayName","ReaSyn",["D",1758309494708],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara/resources/reasyn","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[273,274,275,276],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_242":291,"_278":292,"_279":293,"_281":88},{"_242":287,"_278":288,"_279":289,"_281":88},{"_242":283,"_278":284,"_279":285,"_281":88},{"_242":277,"_278":277,"_279":280,"_281":88},"NSPECT-NZMC-4JKB","value","filter",{"_282":88,"_278":277},"category","field","Healthcare","indus_healthcare",{"_282":286,"_278":284},"industry","Drug Discovery","uscs_drug_discovery",{"_282":290,"_278":288},"application","AI","soln_ai",{"_282":294,"_278":292},"technology",{"_152":1428,"_154":1429},"ngcApps",[1381],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1362":1363,"_1364":1365,"_1366":1367,"_1368":1369,"_1370":306,"_110":332,"_1371":1372,"_1373":59,"_1374":91,"_1375":91,"_1376":1377,"_1378":91,"_1379":1380},"amplitude",{"_1359":1360,"_967":-7,"_1361":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1353":1354,"_1355":1356,"_1357":1358},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1791133562077,"1.19.0","defaultSearchSuggestions",{"_1237":220,"_1238":1239,"_1240":1241,"_1242":1243},"announcements",[],"consentState",{"_370":1227,"_1228":34,"_1229":34,"_1230":91,"_1231":1232},"consentConfig",{"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371},"showContactAdmin",[],[],[345,346,347,348,349,350],{"_48":63,"_50":74,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":351,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},4,"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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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. If you do not allow these cookies, then some or all of these services may not function properly.",[642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672],[608,609,610,611,612,613],{"_412":494,"_242":494,"_211":220,"_384":637},{"_412":469,"_242":469,"_211":220,"_384":632},{"_412":627,"_242":627,"_211":220,"_384":628},{"_412":622,"_242":622,"_211":220,"_384":623},{"_412":618,"_242":618,"_211":220,"_384":619},{"_412":614,"_242":614,"_211":220,"_384":615},"nvcrm.my.site.com",[616],{"_113":617,"_418":614,"_419":34,"_211":220},"BrowserId","nvidia.partners",[620],{"_113":621,"_418":618,"_419":34,"_211":455},"edge","app.hushly.com",[624],{"_113":625,"_418":622,"_419":34,"_211":626},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[629],{"_113":630,"_418":627,"_419":34,"_211":631},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785],[714,715,716,717,718],{"_412":737,"_242":737,"_211":220,"_384":738},{"_412":732,"_242":732,"_211":220,"_384":733},{"_412":482,"_242":482,"_211":220,"_384":729},{"_412":722,"_242":722,"_211":220,"_384":723},{"_412":627,"_242":627,"_211":220,"_384":719},[720],{"_113":721,"_418":627,"_419":91,"_211":631},"_gh_sess","statcounter.com",[724,725],{"_113":728,"_418":722,"_419":34,"_211":727},{"_113":726,"_418":722,"_419":34,"_211":727},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[730],{"_113":731,"_418":482,"_419":91,"_211":488},"YSC","bizible.com",[734],{"_113":735,"_418":732,"_419":34,"_211":736},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[739],{"_113":740,"_418":737,"_419":34,"_211":741},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_113":847,"_418":556,"_419":34,"_211":848},{"_113":845,"_418":556,"_419":91,"_211":846},{"_113":842,"_418":843,"_419":34,"_211":844},{"_113":820,"_418":833,"_419":91,"_211":821},{"_113":840,"_418":556,"_419":34,"_211":841},{"_113":794,"_418":559,"_419":34,"_211":690},{"_113":794,"_418":592,"_419":34,"_211":690},{"_113":838,"_418":556,"_419":34,"_211":839},{"_113":822,"_418":677,"_419":91,"_211":823},{"_113":820,"_418":562,"_419":91,"_211":821},{"_113":836,"_418":556,"_419":34,"_211":837},{"_113":834,"_418":556,"_419":34,"_211":835},{"_113":828,"_418":556,"_419":34,"_211":220},{"_113":819,"_418":833,"_419":34,"_211":463},{"_113":820,"_418":559,"_419":91,"_211":821},{"_113":831,"_418":592,"_419":34,"_211":832},{"_113":829,"_418":682,"_419":34,"_211":830},{"_113":820,"_418":706,"_419":91,"_211":821},{"_113":828,"_418":677,"_419":34,"_211":220},{"_113":792,"_418":559,"_419":34,"_211":793},{"_113":819,"_418":559,"_419":34,"_211":463},{"_113":826,"_418":556,"_419":91,"_211":827},{"_113":824,"_418":556,"_419":34,"_211":825},{"_113":822,"_418":556,"_419":91,"_211":823},{"_113":820,"_418":677,"_419":91,"_211":821},{"_113":819,"_418":677,"_419":34,"_211":463},{"_113":817,"_418":556,"_419":34,"_211":818},{"_113":815,"_418":556,"_419":34,"_211":816},{"_113":813,"_418":556,"_419":91,"_211":814},{"_113":810,"_418":811,"_419":34,"_211":812},{"_113":792,"_418":677,"_419":34,"_211":793},{"_113":808,"_418":556,"_419":91,"_211":809},{"_113":806,"_418":556,"_419":34,"_211":807},{"_113":805,"_418":677,"_419":91,"_211":800},{"_113":803,"_418":556,"_419":91,"_211":804},{"_113":801,"_418":571,"_419":34,"_211":802},{"_113":799,"_418":677,"_419":91,"_211":800},{"_113":797,"_418":556,"_419":34,"_211":798},{"_113":795,"_418":556,"_419":34,"_211":796},{"_113":794,"_418":556,"_419":34,"_211":690},{"_113":792,"_418":571,"_419":34,"_211":793},{"_113":790,"_418":556,"_419":34,"_211":791},{"_113":788,"_418":556,"_419":91,"_211":789},{"_113":786,"_418":556,"_419":34,"_211":787},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.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.\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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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","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.","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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed 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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","programs.nvidia.com","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","buildapi.ngc.nvidia.com","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.","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.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","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","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","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. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","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.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_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.","c_code","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","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. 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