论文标题
用于结构阐明的因子图分子网络
Factor Graph Molecule Network for Structure Elucidation
论文作者
论文摘要
鉴于其物理/化学特性，设计网络以学习分子结构是一个困难的问题，但对于药物发现任务很有用。在本文中，我们将因子图的高阶关系学习与神经网络的强近似能力相结合，以创建具有强大概括能力的分子结构学习网络，并可以强制执行高阶关系和价值约束。我们进一步提出了解决问题的方法，例如因子节点的有效设计，因素之间的条件参数共享以及分子结构预测中的对称问题。我们的实验评估表明，因子学习是有效的，并且胜过相关的方法。
Designing a network to learn a molecule structure given its physical/chemical properties is a hard problem, but is useful for drug discovery tasks. In this paper, we incorporate higher-order relational learning of Factor Graphs with strong approximation power of Neural Networks to create a molecule-structure learning network that has strong generalization power and can enforce higher-order relationship and valence constraints. We further propose methods to tackle problems such as the efficient design of factor nodes, conditional parameter sharing among factors, and symmetry problems in molecule structure prediction. Our experiment evaluation shows that the factor learning is effective and outperforms related methods.
