发表机构
Xiangtan University; Hunan University; Beijing University of Posts and Telecommunications; Southeast University（湘潭大学; 湖南大学; 北京邮电大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结构保留的PhenMol框架，通过解耦分子与细胞表示实现表型引导对齐，在多类任务上提升性能，为药物发现整合细胞表型与化学知识提供了有效途径。
AI 中文摘要
表型药物发现可用于挖掘分子结构与细胞响应间的功能关系，但现有多模态表示学习方法通常仅优化跨模态对齐，未考虑化学空间的内在组织，导致分子表示失真、结构信息丢失。本文提出PhenMol，一种用于表型感知分子表示学习的结构保留框架：PhenMol将分子和细胞表示解耦为共享与私有分量，通过专用分子分支在保留化学结构的同时实现表型引导对齐，该设计可整合细胞表型信息且不破坏分子邻域组织。在约3.04×10^4个分子-细胞形态对的实验中，PhenMol在270项生物活性任务的分子性质预测、分子-表型检索及临床试验结果预测中均表现更优；基于ECFP4的结构分析显示，与现有多模态对齐方法相比，PhenMol能更好地保留分子邻域、降低嵌入失真。这些结果凸显了结构感知约束在多模态分子表示学习中的重要性，为药物发现中整合细胞表型与化学知识提供了有效方法。
英文摘要
Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learning methods often optimize cross-modal alignment without considering the intrinsic organization of chemical space, resulting in distorted molecular representations and loss of structural information. We propose \textbf{PhenMol}, a structure-preserving framework for phenotype-aware molecular representation learning. PhenMol disentangles molecular and cellular representations into shared and private components, enabling phenotype-guided alignment while preserving chemical structures through a dedicated molecular branch. This design integrates cellular phenotype information without disrupting molecular neighborhood organization. Experiments on approximately $3.04 \times 10^{4}$ molecule--cell morphology pairs demonstrate that PhenMol improves molecular property prediction across 270 bioactivity tasks, molecule--phenotype retrieval, and clinical trial outcome prediction. Moreover, ECFP4-based structural analysis shows that PhenMol better preserves molecular neighborhoods and reduces embedding distortion compared with existing multimodal alignment methods. These results highlight the importance of structure-aware constraints in multimodal molecular representation learning and provide an effective approach for integrating cellular phenotypes with chemical knowledge for drug discovery.
