Model Overview

A Graph Neural Network using a variant of self-attention for 3D points and graphs processing.

Model Architecture

The model consists of stacked layers of equivariant graph self-attention and equivariant normalization.\nLastly, a Tensor Field Network convolution is applied to obtain invariant features. Graph pooling (mean or max over the nodes) is applied to these features, and the result is fed to a final MLP to get scalar predictions.

In this setup, the model is a graph-to-scalar network. The pooling can be removed to obtain a graph-to-graph network, and the final TFN can be modified to output features of any type (invariant scalars, 3D vectors, ...).

$\"Model$

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

Quantum Machines 9 - Database providing quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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Graph pooling (mean or max over the nodes) is applied to these features, and the result is fed to a final MLP to get scalar predictions.\n\nIn this setup, the model is a graph-to-scalar network. The pooling can be removed to obtain a graph-to-graph network, and the final TFN can be modified to output features of any type (invariant scalars, 3D vectors, ...).\n\n![Model high-level architecture](https://github.com/NVIDIA/DeepLearningExamples/raw/master/DGLPyTorch/DrugDiscovery/SE3Transformer/images/se3-transformer.png)\n\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/se3transformer_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/DGLPyTorch/DrugDiscovery/SE3Transformer).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [Quantum Machines 9](http://quantum-machine.org/datasets/) - Database providing quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/se3transformer_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/2006.10503)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/se3transformer_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/DGLPyTorch/DrugDiscovery/SE3Transformer)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/DGLPyTorch/DrugDiscovery/SE3Transformer) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","SE(3)-Transformer checkpoint (PyTorch, AMP, QM9 homo task)",["D",1680636787767],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/se3transformer__pyt_ckpt_mode-homo","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":252,"_242":253,"_244":254,"_246":247},{"_206":250,"_242":250,"_244":251,"_246":247},{"_206":241,"_242":243,"_244":245,"_246":247},"Graph Neural Networks","value","uscs_graph_neural_networks","filter",{"_248":249,"_242":243},"category","label","field","application","GNN",{"_248":247,"_242":250},"Drug Discovery","uscs_drug_discovery",{"_248":249,"_242":253},{"_107":1430,"_109":1431},"ngcApps",[1383],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1364":1365,"_1366":1367,"_1368":1369,"_1370":1371,"_1372":266,"_138":290,"_1373":1374,"_1375":56,"_1376":86,"_1377":86,"_1378":1379,"_1380":86,"_1381":1382},"amplitude",{"_1361":1362,"_962":-7,"_1363":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1355":1356,"_1357":1358,"_1359":1360},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1237":168,"_1238":1239,"_1240":1241,"_1242":1243},"announcements",[],"consentState",{"_327":1227,"_1228":86,"_1229":31,"_1230":31,"_1231":1232},"consentConfig",{"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328},"showContactAdmin",1784271263961,[],[],[304,305,306,307,308],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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