# Learning Molecular Representations from Cellular Phenotypes with Structure Preservation

arXiv: https://arxiv.org/abs/2608.02688 · HTML: https://mlsanity.com/paper/2608.02688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Graph Contrastive Learning Automated](https://mlsanity.com/paper/2106.07594/) (2021)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction](https://mlsanity.com/paper/2206.03364/) (2022)
- [Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge](https://mlsanity.com/paper/2406.09841/) (2024)
- [Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables](https://mlsanity.com/paper/2505.12473/) (2025)
- [Learning Better Contrastive View from Radiologist's Gaze](https://mlsanity.com/paper/2305.08826/) (2023)
- [Molecular Graph Contrastive Learning with Line Graph](https://mlsanity.com/paper/2501.08589/) (2025)

## Abstract (arXiv)

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 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 × 104 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.