From molecules to scaffolds to functional groups: building
  context-dependent molecular representation via multi-channel learning
Yue Wan Jialu Wu Tingjun Hou Chang-Yu Hsieh Xiaowei Jia
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.