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Structural complementarity-aware molecular representation learning for medication recommendation
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AI
DOI：10.1016/j.engappai.2026.114840.png)
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Abstract
En 中文
To achieve accurate medication recommendations, recent studies have focused on extracting appropriate molecular structure embeddings to capture pharmacological mechanisms. Building upon two-dimensional (2D) modality representations, existing works incorporate three-dimensional (3D) structural information and employ contrastive learning to derive modality-invariant embeddings. In this paper, we identify two key limitations of existing multimodal molecular representation methods. First, different modalities of the same molecule contain shared features conveying common semantics and modality-specific features providing complementary structural information. Undifferentiated alignment strategies lead to the loss of modality-specific information. Second, naive concatenation of embeddings may cause modality collapse, where the richer 3D modality dominates and suppresses the 2D modality. To overcome these issues, we propose Structural Complementarity-Aware molecular representation learning for Medication recommendation (SCAMed), a framework designed to achieve precise alignment of shared information, effective extraction of structurally complementary modality-specific information, and balanced multimodal fusion. Specifically, during the representation extraction stage, we introduce an orthogonal decomposition module that separates the 2D and 3D molecular encodings into three disentangled components: 2D-specific, 3D-specific, and shared features. In the fusion stage, we design a reconstruction-based learning strategy that enforces the fused representation to accurately reconstruct the original 2D and 3D embeddings, effectively mitigating modality collapse. Finally, the fused molecular representations are integrated with patient Electronic Health Record (EHR) data for medication prediction. Comprehensive experiments on the Medical Information Mart for Intensive Care III (MIMIC-III) and Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets demonstrate that our framework achieves substantial improvements over state-of-the-art baselines.
Keywords:
molecular representation
medication recommendation
multimodal learning
contrastive learning
modality-specific features
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