  Accurate prediction of molecular properties is critical in the field of drug
discovery. However, existing methods do not fully consider the fact that
molecules in the real world usually possess multiple property labels, and
complex high-order relationships may exist among these labels. Therefore,
molecular representation learning models should generate differential molecular
representations that consider multi-granularity correlation information among
tasks. To this end, our research introduces a Hierarchical Prompted Molecular
Representation Learning Framework (HiPM), which enhances the differential
expression of tasks in molecular representations through task-aware prompts,
and utilizes shared information among labels to mitigate negative transfer
between different tasks. HiPM primarily consists of two core components: the
Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). The
MRE employs a hierarchical message-passing network architecture to capture
molecular features at both the atomic and motif levels, while the TAP uses
agglomerative hierarchical clustering to build a prompt tree that reflects the
affinity and distinctiveness of tasks, enabling the model to effectively handle
the complexity of multi-label property predictions. Extensive experiments
demonstrate that HiPM achieves state-of-the-art performance across various
multi-label datasets, offering a new perspective on multi-label molecular
representation learning.
