论文标题
从蛋白质结构中学习几何矢量感知
Learning from Protein Structure with Geometric Vector Perceptrons
论文作者
论文摘要
在大型生物分子的3D结构上学习正在成为机器学习中的一个独特领域，但是尚未出现统一的网络体系结构，同时利用问题域的图形结构和几何方面。为了解决这一差距，我们引入了几何矢量感知器，该矢量感知器扩展了标准密集层以在欧几里得矢量的集合上操作。配备了此类层的图形神经网络能够对大分子结构的有效和自然表示同时进行几何和关系推理。我们展示了我们从蛋白质结构学习的两个重要问题的方法：模型质量评估和计算蛋白质设计。我们的方法改善了现有的体系结构类别，包括基于图形的最先进和基于体素的方法。我们在https://github.com/drorlab/gvp上发布代码。
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors. Graph neural networks equipped with such layers are able to perform both geometric and relational reasoning on efficient and natural representations of macromolecular structure. We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design. Our approach improves over existing classes of architectures, including state-of-the-art graph-based and voxel-based methods. We release our code at https://github.com/drorlab/gvp.
