N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
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发表时间：
2018-06
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通讯作者：
Shengchao Liu;M. F. Demirel;Yingyu Liang
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作者：
Shengchao Liu;M. F. Demirel;Yingyu Liang
机器学习技术最近已被用于医学、生物学、化学和材料工程的各种应用中。预测分子的性质是一项重要的任务，在许多下游应用中，如虚拟筛选和药物设计，这是主要的子程序。尽管越来越多的兴趣，关键的挑战是构建正确的分子表示学习算法。本文介绍了N-gram图，一种简单的分子无监督表示。该方法首先将顶点嵌入到分子图中。然后，它通过在图中的短路径中组装顶点嵌入来构造图的紧凑表示，我们证明这相当于一个不需要训练的简单图神经网络。因此，可以有效地计算表示，然后与监督学习方法一起用于预测。在10个基准数据集上的60个任务上的实验表明，该方法优于流行的图神经网络和传统的表示方法。这是补充理论分析显示其强大的代表性和预测能力。
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite the increasing interest, the key challenge is to construct proper representations of molecules for learning algorithms. This paper introduces the N-gram graph, a simple unsupervised representation for molecules. The method first embeds the vertices in the molecule graph. It then constructs a compact representation for the graph by assembling the vertex embeddings in short walks in the graph, which we show is equivalent to a simple graph neural network that needs no training. The representations can thus be efficiently computed and then used with supervised learning methods for prediction. Experiments on 60 tasks from 10 benchmark datasets demonstrate its advantages over both popular graph neural networks and traditional representation methods. This is complemented by theoretical analysis showing its strong representation and prediction power.