Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing

• URL: http://arxiv.org/abs/2511.08080v1
• Date: Wed, 12 Nov 2025 01:38:29 GMT
• Title: Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing
• Authors: Ziyu Fan, Zhijian Huang, Yahan Li, Xiaowen Hu, Siyuan Shen, Yunliang Wang, Zeyu Zhong, Shuhong Liu, Shuning Yang, Shangqian Wu, Min Wu, Lei Deng,
• Abstract summary: HSPAG is a data-efficient framework featuring hierarchical structure-property alignment.<n>We select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder.<n> Experiments demonstrate that HSPAG captures fine-grained structure-property relationships and supports controllable generation under multiple property constraints.
• Score: 14.308978798996472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure-property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure-property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG.

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