Learning Molecular Representations from Cellular Phenotypes with Structure Preservation

• URL: http://arxiv.org/abs/2608.02688v1
• Date: Mon, 03 Aug 2026 08:33:54 GMT
• Title: Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
• Abstract summary: PhenMol is a structure-preserving framework for phenotype-aware molecular representation learning.<n>It integrates cellular phenotype information without disrupting molecular neighborhood organization.<n>These results highlight the importance of structure-aware constraints in multimodal molecular representation learning.
• Score: 13.321582594948218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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