让不懂深度学习的科研人员也能用三维等变模型做分子研究
DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library
- 在DeepChem中加入可直接使用的三维等变神经网络
- 支持SE(3)-Transformer等模型，提供完整训练流程
- 适合分子、蛋白和材料领域的研究人员快速上手
能够尊重SE(3)群变换（如旋转和平移）的神经网络在分子性质预测、蛋白质结构建模和材料设计中日益重要。这类称为SE(3)-等变神经网络的模型通过显式编码原子空间位置，确保输出随输入坐标变化而可预测地改变。尽管E3NN和SE(3)-TRANSFORMER等库提供了强大实现，但通常需要深厚的深度学习或数学背景，且缺乏完整的训练流程。我们扩展了DeepChem，加入开箱即用的等变模型支持，使具备最少深度学习背景的科研人员也能构建、训练和评估SE(3)-Transformer和张量场网络等模型。实现包括等变模型、完整训练管道和一系列等变工具，配有全面测试与文档，促进SE(3)-等变模型的应用与进一步开发。
原文摘要 · Abstract (English)
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.
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