DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918163876151296 |
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| author | Siguenza, Jose Ramsundar, Bharath |
| author_facet | Siguenza, Jose Ramsundar, Bharath |
| contents | Neural networks that incorporate geometric relationships respecting SE(3) group transformations (e.g. rotations and translations) are increasingly important in molecular applications, such as molecular property prediction, protein structure modeling, and materials design. These models, known as SE(3)-equivariant neural networks, ensure outputs transform predictably with input coordinate changes by explicitly encoding spatial atomic positions. Although libraries such as E3NN [4] and SE(3)-TRANSFORMER [3 ] offer powerful implementations, they often require substantial deep learning or mathematical prior knowledge and lack complete training pipelines. We extend DEEPCHEM [ 13] with support for ready-to-use equivariant models, enabling scientists with minimal deep learning background to build, train, and evaluate models, such as SE(3)-Transformer and Tensor Field Networks. Our implementation includes equivariant models, complete training pipelines, and a toolkit of equivariant utilities, supported with comprehensive tests and documentation, to facilitate both application and further development of SE(3)-equivariant models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library Siguenza, Jose Ramsundar, Bharath Machine Learning Neural networks that incorporate geometric relationships respecting SE(3) group transformations (e.g. rotations and translations) are increasingly important in molecular applications, such as molecular property prediction, protein structure modeling, and materials design. These models, known as SE(3)-equivariant neural networks, ensure outputs transform predictably with input coordinate changes by explicitly encoding spatial atomic positions. Although libraries such as E3NN [4] and SE(3)-TRANSFORMER [3 ] offer powerful implementations, they often require substantial deep learning or mathematical prior knowledge and lack complete training pipelines. We extend DEEPCHEM [ 13] with support for ready-to-use equivariant models, enabling scientists with minimal deep learning background to build, train, and evaluate models, such as SE(3)-Transformer and Tensor Field Networks. Our implementation includes equivariant models, complete training pipelines, and a toolkit of equivariant utilities, supported with comprehensive tests and documentation, to facilitate both application and further development of SE(3)-equivariant models. |
| title | DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.16897 |