KPGT：知识引导的分子性质预测图Transformer预训练
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
摘要：
设计用于分子性质预测的精确深度学习模型在药物和材料发现中发挥着越来越重要的作用。最近，由于标记分子的稀缺性，用于学习分子图的可推广和可转移表示的自监督学习方法引起了人们的广泛关注。在本文中，我们认为，存在两个主要问题，阻碍目前的自监督学习方法从获得理想的性能的分子性质预测，即，不明确的预训练任务和有限的模型容量。为此，我们引入知识引导的图Transformer（KPGT）的预训练，这是一种用于分子图表示学习的新型自监督学习框架，以缓解上述问题并提高下游分子性质预测任务的性能。更具体地说，我们首先介绍了一个高容量的模型，命名为线图Transformer（LiGhT），它强调了化学键的重要性，主要是为了模拟分子图的结构信息。然后，提出了一种知识引导的预训练策略，利用分子的额外知识来指导模型从大规模未标记分子图中捕获丰富的结构和语义信息。广泛的计算测试表明，KPGT可以提供优于当前国家的最先进的方法上的几个分子性质预测任务的性能。
英文摘要：
Designing accurate deep learning models for molecular property prediction plays an increasingly essential role in drug and material discovery. Recently, due to the scarcity of labeled molecules, self-supervised learning methods for learning generalizable and transferable representations of molecular graphs have attracted lots of attention. In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity. To this end, we introduce Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel self-supervised learning framework for molecular graph representation learning, to alleviate the aforementioned issues and improve the performance on the downstream molecular property prediction tasks. More specifically, we first introduce a high-capacity model, named Line Graph Transformer (LiGhT), which emphasizes the importance of chemical bonds and is mainly designed to model the structural information of molecular graphs. Then, a knowledge-guided pre-training strategy is proposed to exploit the additional knowledge of molecules to guide the model to capture the abundant structural and semantic information from large-scale unlabeled molecular graphs. Extensive computational tests demonstrated that KPGT can offer superior performance over current state-of-the-art methods on several molecular property prediction tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
