融合分子与细胞响应，构建分层多模态模型提升药物预测精度
Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling
- 设计树状向量量化模块，捕捉分子-细胞-基因的层级关系
- 在728个任务上平均提升分类3.6%、回归17.2%性能
- 适合需要生物机制可解释性的药物研发与分子建模研究
理解化学扰动在生物系统中的传播对稳健的分子性质预测至关重要。尽管现有方法主要关注化学结构，但近期研究强调了形态和基因表达等细胞响应在塑造药物效应中的关键作用。然而，当前细胞感知方法存在两大局限：(1) 外部生物数据模态不完整；(2) 对分子、细胞和基因层面间层次依赖建模不足。我们提出CHMR（Cell-aware Hierarchical Multi-modal Representations）框架，联合建模分子与细胞响应的局部-全局依赖关系，并通过新型树状向量量化模块捕捉潜在生物层级结构。在涵盖728个任务的九个公开基准上评估，CHMR显著优于现有最优基线，在分类任务上平均提升3.6%，回归任务上提升17.2%。结果表明，层次感知的多模态学习能生成更可靠、更具生物学意义的分子表示，为整合性生物医学建模提供通用框架。代码见https://github.com/limengran98/CHMR。
原文摘要 · Abstract (English)
Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical structures alone, recent advances highlight the crucial role of cellular responses such as morphology and gene expression in shaping drug effects. However, current cell-aware approaches face two key limitations: (1) modality incompleteness in external biological data, and (2) insufficient modeling of hierarchical dependencies across molecular, cellular, and genomic levels. We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that jointly models local-global dependencies between molecules and cellular responses and captures latent biological hierarchies via a novel tree-structured vector quantization module. Evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines, yielding average improvements of 3.6% on classification and 17.2% on regression tasks. These results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations, offering a generalizable framework for integrative biomedical modeling. The code is in https://github.com/limengran98/CHMR.
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