  Reliable molecular property prediction is essential for various scientific
endeavors and industrial applications, such as drug discovery. However, the
data scarcity, 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 chemical
knowledge, including molecular structure similarity, scaffold composition, and
the context-dependent aspects of molecular properties when operating over the
chemical space. They also struggle to learn the subtle variations in
structure-activity relationship. This paper introduces a novel pre-training
framework that learns robust and generalizable chemical knowledge. It leverages
the structural hierarchy within the molecule, embeds them through distinct
pre-training tasks across channels, and aggregates channel information in a
task-specific manner during fine-tuning. Our approach demonstrates competitive
performance across various molecular property benchmarks and offers strong
advantages in particularly challenging yet ubiquitous scenarios like activity
cliffs.
