论文标题
分子属性预测的模棱两可的图形注意力网络
Equivariant Graph Attention Networks for Molecular Property Prediction
论文作者
论文摘要
关于规模不同的3D分子结构的学习和推理是机器学习，尤其是药物发现中的一个新兴而重要的挑战。模棱两可的图形神经网络（GNNS）可以同时利用问题域的几何和关系细节，并通过利用高阶表示的节点之间的信息传播来忠实地表达数据的几何形状，例如其中介层中的方向性。在这项工作中，我们提出了一个具有笛卡尔坐标以结合方向性并实现新颖的注意机制的均衡性GNN，在传播节点之间的信息时，我们实施了一种新颖的注意机制，充当内容和空间依赖性过滤器。我们证明了建筑在预测小分子的量子机械性能及其对涉及大分子结构（例如蛋白质复合物）的问题上的疗效。
Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through the propagation of information between nodes leveraging higher-order representations to faithfully express the geometry of the data, such as directionality in their intermediate layers. In this work, we propose an equivariant GNN that operates with Cartesian coordinates to incorporate directionality and we implement a novel attention mechanism, acting as a content and spatial dependent filter when propagating information between nodes. We demonstrate the efficacy of our architecture on predicting quantum mechanical properties of small molecules and its benefit on problems that concern macromolecular structures such as protein complexes.
