Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction

TL;DR

The paper introduces HiPM, a hierarchical prompted molecular representation learning framework that improves multi-label property prediction by capturing complex task correlations and mitigating negative transfer.

Contribution

It proposes a novel hierarchical prompting framework with a message-passing encoder and task-aware prompts, advancing multi-label molecular property prediction.

Findings

Achieves state-of-the-art results on multiple datasets.

Effectively models complex task correlations.

Reduces negative transfer in multi-label learning.

Abstract

Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for hierarchical prompted molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning.