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Overview

Description:

DualBind is a state-of-the-art 3D structure-based deep learning model that predicts protein-ligand binding affinity, which plays a critical role in drug discovery. It leverages 3D structural information and employs a dual-loss framework to effectively learn the binding energy landscape. Trained on AB-FEP-calculated labels, DualBind achieves accurate and generalizable predictions at a fraction of the computational cost of physics-based approaches.

This model is ready for non-commercial use and is for research and development only.

License/Terms of Use:

DualBind is released under NSCLv1.

Deployment Geography:

Global

Use Case:

DualBind can be used by researchers and practitioners interested in predicting protein-ligand binding affinities.

Reference(s):

The associated paper can be found here.

[1] Meng Liu, Karl Leswing, Simon KS Chu, Farhad Ramezanghorbani, Griffin Young, Gabriel Marques, Prerna Das et al. \"ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha.\" arXiv preprint arXiv:2507.08966 (2025).

Model Architecture:

Architecture Type: Graph Neural Networks (GNN)
\nNetwork Architecture: Transformer, Frame Averaging Neural Network (FANN)

DualBind employs a dual-loss framework, which combines supervised mean squared error (MSE) loss with unsupervised denoising score matching (DSM) loss to effectively learn the binding energy function. The network architecture is a 3D-invariant graph neural network. Specifically, it is built based on Frame Averaging Neural Network (FANN), within which Transformer layers are used.

Input:

Input Type(s): Text (Protein, Ligand)
\nInput Format(s): Text: String (Protein Data Bank (PDB) files for protein), String (Structural Data Files (SDF) for ligand)
\nInput Parameters: One-Dimensional (1D) (SDF and PDB files)
\nOther Properties Related to Input: The PDB file includes the 3D structure information of the protein and the SDF file includes the 3D structure information of the ligand.

Output:

Output Type(s): Number
\nOutput Format: Number: Floating number
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: The floating number represents the predicted binding affinity.

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:
\nNVIDIA Ampere (tested on A100)

[Preferred/Supported] Operating System(s):

[Linux]

Model Version(s):

DualBind v1.0 (trained on ToxBench, with ~1M parameters)

Training and Testing Datasets:

DualBind is trained and tested on the ToxBench dataset.

Training/Testing Dataset:

Link: ToxBench
\nData Collection Method by dataset:\nSynthetic (complex structures are generated by Schrodinger’s docking method)
\nLabeling Method by dataset:\nSynthetic (affinity labels are computed by Schrodinger’s physics-based computational method, ABFEP)
\nProperties: ToxBench is the first large-scale AB-FEP dataset designed for ML development and focused on a single pharmaceutically critical target, Human Estrogen Receptor Alpha (ERa). ToxBench contains 8,770 ERa-ligand complex structures with binding free energies computed via AB-FEP. Using a 70%/15%/15% random split and ensuring no SMILES overlap, we obtain 5,651 training data, 1,202 validation data, and 1,204 test data.

Inference:

Engine: PyTorch
\nTest Hardware: 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 that predictions given by DualBind are appropriately evaluated and used in compliance with relevant safety regulations and ethical standards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### License/Terms of Use:\nDualBind is released under NSCLv1.
\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nDualBind can be used by researchers and practitioners interested in predicting protein-ligand binding affinities.
\n\n## Reference(s):\nThe associated paper can be found [here](https://arxiv.org/abs/2507.08966).
\n\n[1] Meng Liu, Karl Leswing, Simon KS Chu, Farhad Ramezanghorbani, Griffin Young, Gabriel Marques, Prerna Das et al. \"ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha.\" arXiv preprint arXiv:2507.08966 (2025).\n\n## Model Architecture:
\n**Architecture Type:** Graph Neural Networks (GNN)
\n**Network Architecture:** Transformer, Frame Averaging Neural Network (FANN)
\n\nDualBind employs a dual-loss framework, which combines supervised mean squared error (MSE) loss with unsupervised denoising score matching (DSM) loss to effectively learn the binding energy function. The network architecture is a 3D-invariant graph neural network. Specifically, it is built based on Frame Averaging Neural Network (FANN), within which Transformer layers are used.\n\n\n## Input:
\n**Input Type(s):** Text (Protein, Ligand)
\n**Input Format(s):** Text: String (Protein Data Bank (PDB) files for protein), String (Structural Data Files (SDF) for ligand)
\n**Input Parameters:** One-Dimensional (1D) (SDF and PDB files)
\n**Other Properties Related to Input:** The PDB file includes the 3D structure information of the protein and the SDF file includes the 3D structure information of the ligand.
\n\n\n## Output:
\n**Output Type(s):** Number
\n**Output Format:** Number: Floating number
\n**Output Parameters:** One-Dimensional (1D)
\n**Other Properties Related to Output:** The floating number represents the predicted binding affinity.
\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):** PyTorch
\n\n**Supported Hardware Microarchitecture Compatibility:**
\nNVIDIA Ampere (tested on A100)
\n\n**[Preferred/Supported] Operating System(s):**
\n* [Linux]
\n\n## Model Version(s): \nDualBind v1.0 (trained on ToxBench, with ~1M parameters)
\n\n# Training and Testing Datasets:
\nDualBind is trained and tested on the [ToxBench dataset](https://arxiv.org/abs/2507.08966). \n\n## Training/Testing Dataset:\n\n**Link:** [ToxBench](https://huggingface.co/datasets/karlleswing/toxbench)
\n**Data Collection Method by dataset**: \nSynthetic (complex structures are generated by Schrodinger’s docking method)
\n**Labeling Method by dataset**: \nSynthetic (affinity labels are computed by Schrodinger’s physics-based computational method, ABFEP)
\n**Properties:** ToxBench is the first large-scale AB-FEP dataset designed for ML development and focused on a single pharmaceutically critical target, Human Estrogen Receptor Alpha (ERa). ToxBench contains 8,770 ERa-ligand complex structures with binding free energies computed via AB-FEP. Using a 70%/15%/15% random split and ensuring no SMILES overlap, we obtain 5,651 training data, 1,202 validation data, and 1,204 test data.
\n\n## Inference:\n**Engine:** PyTorch
\n**Test Hardware:** 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 that predictions given by DualBind are appropriately evaluated and used in compliance with relevant safety regulations and ethical standards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","DualBind Model Weights",["D",1752796468377],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/resources/dualbind_v1_toxbench","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[234,235,236,237,238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_203":262,"_241":263,"_242":264,"_244":245},{"_203":258,"_241":259,"_242":260,"_244":245},{"_203":254,"_241":255,"_242":256,"_244":245},{"_203":250,"_241":251,"_242":252,"_244":245},{"_203":248,"_241":248,"_242":249,"_244":245},{"_203":240,"_241":240,"_242":243,"_244":245},"PyTorch","value","filter",{"_246":247,"_241":240},"category","label","field","framework","NSPECT-V2WJ-JPIA",{"_246":245,"_241":248},"Healthcare","indus_healthcare",{"_246":253,"_241":251},"industry","Drug Discovery","uscs_drug_discovery",{"_246":257,"_241":255},"application","Clara","pltfm_clara",{"_246":261,"_241":259},"platform","AI","soln_ai",{"_246":265,"_241":263},"technology",{"_106":1439,"_108":1440},"ngcApps",[1392],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1373":1374,"_1375":1376,"_1377":1378,"_1379":1380,"_1381":277,"_137":301,"_1382":1383,"_1384":55,"_1385":85,"_1386":85,"_1387":1388,"_1389":85,"_1390":1391},"amplitude",{"_1370":1371,"_973":-7,"_1372":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1364":1365,"_1366":1367,"_1368":1369},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1248":178,"_1249":1250,"_1251":1252,"_1253":1254},"announcements",[],"consentState",{"_339":1238,"_1239":85,"_1240":30,"_1241":30,"_1242":1243},"consentConfig",{"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340},"showContactAdmin",1784111194435,[],[],[315,316,317,318,319,320],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":101,"_50":-5,"_52":-5,"_54":55},"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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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. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","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.","AWS ELB application load balancer","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_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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