Reliable molecular property prediction is essential for various scientific
endeavors and industrial applications, such as drug discovery. However, the
scarcity of data, combined with the highly non-linear causal relationships
between physicochemical and biological properties and conventional molecular
featurization schemes, complicates the development of robust molecular machine
learning models. Self-supervised learning (SSL) has emerged as a popular
solution, utilizing large-scale, unannotated molecular data to learn a
foundational representation of chemical space that might be advantageous for
downstream tasks. Yet, existing molecular SSL methods largely overlook
domain-specific knowledge, such as molecular similarity and scaffold
importance, as well as the context of the target application when operating
over the large chemical space. This paper introduces a novel learning framework
that leverages the knowledge of structural hierarchies within molecular
structures, embeds them through separate pre-training tasks over distinct
channels, and employs a task-specific channel selection to compose a
context-dependent representation. Our approach demonstrates competitive
performance across various molecular property benchmarks and establishes some
state-of-the-art results. It further offers unprecedented advantages in
particularly challenging yet ubiquitous scenarios like activity cliffs with
enhanced robustness and generalizability compared to other baselines.

使用自监督学习和分子结构内的层次知识，该论文提出了一种新的学习框架，从而可以准确预测分子属性并在各种基准测试中取得竞争性表现。