Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and prediction tasks. In cases where labeled data is scarce, GNNs can be pre-trained on unlabeled molecular data to first learn the general semantic and structural information before being fine-tuned for specific tasks. However, most existing self-supervised pre-training frameworks for GNNs only focus on node-level or graph-level tasks. These approaches cannot capture the rich information in subgraphs or graph motifs. For example, functional groups (frequently-occurred subgraphs in molecular graphs) often carry indicative information about the molecular properties. To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs. First, for motif extraction from molecular graphs, we design a molecule fragmentation method that leverages a retrosynthesis-based algorithm BRICS and additional rules for controlling the size of motif vocabulary. Second, we design a general motif-based generative pre-training framework in which GNNs are asked to make topological and label predictions. This generative framework can be implemented in two different ways, i.e., breadth-first or depth-first. Finally, to take the multi-scale information in molecular graphs into consideration, we introduce a multi-level self-supervised pre-training. Extensive experiments on various downstream benchmark tasks show that our methods outperform all state-of-the-art baselines.
翻译:使用数据驱动方法的预测分子性质近年来引起了人们的极大注意。 特别是, 图形神经网络( GNN) 在各种分子生成和预测任务中表现出了显著的成功。 在标签数据稀缺的情况下, GNNN可以先在未贴标签的分子数据上接受培训, 以便首先学习一般语义和结构信息, 然后对具体任务进行微调。 然而, 大多数现有的GNNN的自监督前培训框架只侧重于节点级别或图形级别的任务。 这些方法无法捕捉子图或图形模型模型中的丰富信息。 例如, 功能组( 分子图中反复出现的多层子图) 往往会包含关于分子属性的指示性信息。 为了缩小这一差距, 我们建议以 Motif 为基础的图形自监督学习( MGSSL ), 引入一个全新的自上层模型生成框架。 首先, 从分子图中提取的 motif, 我们设计一个分子分裂方法, 利用回溯的 iynex 水平 水平, 多层 数据结构框架 引入了第二个 GNBR 和额外规则, 用于控制我们基于 IMal 的 IMal 的G IML IM 的基 的模型 的预,,, 的模型,, 的模型的模型 的模型的模型的模型,,,, 开始 开始, 。 开始 开始 开始 开始 开始 。 开始 。