Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure-property relationships across diverse molecular modalities. Existing approaches frequently exhibit entangled representations--conflating structural, chemical, and functional factors--thereby limiting interpretability and transferability. Furthermore, conventional methods inadequately exploit complementary information from graphs, sequences, and geometries, often relying on naive concatenation that neglects inter-modal dependencies. In this work, we propose DMMRL, which employs variational autoencoders to disentangle molecular representations into shared (structure-relevant) and private (modality-specific) latent spaces, enhancing both interpretability and predictive performance. The proposed variational disentanglement mechanism effectively isolates the most informative features for property prediction, while orthogonality and alignment regularizations promote statistical independence and cross-modal consistency. Additionally, a gated attention fusion module adaptively integrates shared representations, capturing complex inter-modal relationships. Experimental validation across seven benchmark datasets demonstrates DMMRL's superior performance relative to state-of-the-art approaches. The code and data underlying this article are freely available at https://github.com/xulong0826/DMMRL.

翻译：分子性质预测是药物发现与材料科学的重要基石，需要模型能够从多种分子模态中解耦复杂的结构-性质关系。现有方法常产生纠缠表征——将结构、化学和功能因素混为一谈——从而限制了可解释性与可迁移性。此外，传统方法未能充分利用图、序列和几何结构中的互补信息，往往采用忽略模态间依赖关系的简单拼接。本文提出DMMRL方法，采用变分自编码器将分子表征解耦为共享（结构相关）和私有（模态特异）潜在空间，兼顾了可解释性与预测性能。所提出的变分解耦机制能够有效分离最有利于性质预测的信息特征，同时通过正交性与对齐正则化促进统计独立性和跨模态一致性。此外，门控注意力融合模块自适应整合共享表征，捕捉复杂的模态间关系。在七个基准数据集上的实验验证表明，DMMRL相较于当前最优方法具有更优越的性能。本文相关代码与数据公开获取于https://github.com/xulong0826/DMMRL。