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

• URL: http://arxiv.org/abs/2405.18724v2
• Date: Sun, 11 Aug 2024 07:02:54 GMT
• Title: Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction
• Authors: Linjia Kang, Songhua Zhou, Shuyan Fang, Shichao Liu,
• Abstract summary: HiPM stands for hierarchical prompted molecular representation learning framework. Our framework comprises two core components: the Molecular Representation (MRE) and the Task-Aware Prompter (TAP)
• Score: 2.344198904343022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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