Pretrained language models (LMs) showcase significant capabilities in
processing molecular text, while concurrently, message passing neural networks
(MPNNs) demonstrate resilience and versatility in the domain of molecular
science. Despite these advancements, we find there are limited studies
investigating the bidirectional interactions between molecular structures and
their corresponding textual representations. Therefore, in this paper, we
propose two strategies to evaluate whether an information integration can
enhance the performance: contrast learning, which involves utilizing an MPNN to
supervise the training of the LM, and fusion, which exploits information from
both models. Our empirical analysis reveals that the integration approaches
exhibit superior performance compared to baselines when applied to smaller
molecular graphs, while these integration approaches do not yield performance
enhancements on large scale graphs.

预训练语言模型和信息传递神经网络在处理分子文本和分子科学领域展示了显著的能力。本文提出了两种策略来评估信息整合是否能增强性能：对比学习和融合，经实证分析表明，当应用于较小的分子图时，整合方法相比基准模型表现出优越性能，而当应用于大规模图时，这些整合方法并未带来性能改进。